Load the following libraries. If they are not installed, run install.packages (“packagename”)
#from https://www.vikram-baliga.com/blog/2015/7/19/a-hassle-free-way-to-verify-that-r-packages-are-installed-and-loaded
packages = c("tidyverse","ggplot2","beeswarm","colorspace", "stargazer", "qwraps2", "gridExtra","ggpubr","car","olsrr","ggbeeswarm", "broom", "rmarkdown", "table1", "kableExtra", "epitools", "FSA", "Hmisc", "userfriendlyscience")
package.check <- lapply(packages, FUN = function(x) {
if (!require(x, character.only = TRUE)) {
install.packages(x, dependencies = TRUE)
library(x, character.only = TRUE)
}
})
#verify they are loaded
search()
First, import the data from the data (CSV) file
setwd("/Users/emilycarrigan/Dropbox/Data Analysis Work/") #This should be wherever your file is saved
BRIEF <- read.csv("BRIEF_SLaM&CGdata_CG_200423.csv", na.strings = c("N/A", "", "Unknown", "Excluded"))
BRIEF <- subset(BRIEF, BRIEF$AgeMonths < 100 & BRIEF$AgeMonths!="" & BRIEF$SES..3.66.!="")
BRIEF$Language_Modality <- factor(ifelse(BRIEF$Group_4cat == "English Early" | BRIEF$Group_4cat == "English Later", "English", "ASL"), levels = c("English", "ASL"))
BRIEF$Language_Timing <- factor(as.character(BRIEF$Group_2cat), levels = c("Early", "Later"), exclude="")
BRIEF$LanguageGroup <- as.factor(factor(as.character(BRIEF$Group_4cat), levels = c("English Early", "ASL Early", "English Later", "ASL Later"), labels = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), exclude=NA))
BRIEF$Hearing_Level_3_Cat <- as.factor(factor(as.character(BRIEF$Hearing_Level_3_Cat), levels = c("TH", "deaf", "HoH"), labels = c("Typically Hearing", "Deaf", "Hard of Hearing"), exclude=NA))
BRIEF$Hearing.Device <- dplyr::recode(BRIEF$Hearing.Device, "BAHA" = "Other")
BRIEF$Hearing.Device <- as.factor(factor(as.character(BRIEF$Hearing.Device), levels = c("HA", "CI", "HA & CI", "Other", "None"), labels = c("Hearing Aid", "Cochlear Implant", "Hearing Aid & Cochlear Implant", "Other", "None"), exclude=NA))
#Recode Race to have fewer categories
BRIEF$Race_recoded <- dplyr::recode(as.character(BRIEF$Race), 'Asian' = "Asian", 'Black or African American'="Black or African American", 'More than One'="More than one", 'Other'="Other/Missing", 'Unsure, or prefer not to answer' = "Other/Missing", 'White'="White", .missing="Other/Missing")
#update ORDER of groups
BRIEF$Race_recoded <- as.factor(factor(as.character(BRIEF$Race_recoded), levels = c("Asian", "Black or African American", "White", "More than one", "Other/Missing"), exclude=NA))
#Recode Ethnicity to have fewer categories
BRIEF$Ethnicity_recoded <- dplyr::recode(as.character(BRIEF$Ethnicity), 'Hispanic' = "Hispanic", 'NonHispanic'="Non-Hispanic", 'PreferNotToAnswer'="Prefer not to answer", .missing="Missing")
#update ORDER of groups
BRIEF$Ethnicity_recoded <- as.factor(factor(as.character(BRIEF$Ethnicity_recoded), levels = c("Hispanic", "Non-Hispanic", "Prefer not to answer", "Missing"), exclude=NA))
Create dataframe for participants with Age of Auditory Exposure and Age of Language Exposure
BRIEF <- dplyr::mutate(BRIEF, AoAE = ifelse(BRIEF$Age.of.Auditory.Exposure..mo.=="None", as.character(BRIEF$AgeMonths), as.character(BRIEF$Age.of.Auditory.Exposure..mo.)))
BRIEF$AoAE <- as.integer(BRIEF$AoAE)
BRIEF_AgeOf <- subset(BRIEF, BRIEF$AoAE!='' & BRIEF$Age.of.Language.Exposure..mo.!='')
BRIEF_AgeApp <- subset(BRIEF_AgeOf, BRIEF_AgeOf$AgeMonths <= 71)
table1::label(BRIEF$AgeMonths) <- "Age (Months)"
table1::label(BRIEF$SES..3.66.) <- "SES"
table1::label(BRIEF$Sex) <- "Sex"
table1::label(BRIEF$Race_recoded) <- "Race"
table1::label(BRIEF$Ethnicity_recoded) <- "Ethnicity"
table1::table1(~AgeMonths + SES..3.66. + Sex + Race_recoded + Ethnicity_recoded | LanguageGroup, data = BRIEF, overall=F)
| Typically Hearing (N=46) |
Early ASL (N=28) |
Later English (N=35) |
Later ASL (N=30) |
|
|---|---|---|---|---|
| Age (Months) | ||||
| Mean (SD) | 54.7 (10.9) | 61.2 (14.2) | 58.5 (11.8) | 66.7 (15.1) |
| Median [Min, Max] | 54.5 [37.0, 85.0] | 58.0 [41.0, 91.0] | 60.0 [37.0, 78.0] | 71.0 [37.0, 90.0] |
| SES | ||||
| Mean (SD) | 55.6 (8.83) | 48.8 (16.3) | 47.7 (15.8) | 43.7 (17.2) |
| Median [Min, Max] | 56.0 [21.5, 66.0] | 56.5 [11.0, 66.0] | 53.0 [3.00, 66.0] | 49.8 [9.00, 62.0] |
| Sex | ||||
| Female | 24 (52.2%) | 18 (64.3%) | 20 (57.1%) | 13 (43.3%) |
| Male | 22 (47.8%) | 10 (35.7%) | 15 (42.9%) | 17 (56.7%) |
| Race | ||||
| Asian | 0 (0%) | 0 (0%) | 1 (2.9%) | 4 (13.3%) |
| Black or African American | 0 (0%) | 0 (0%) | 2 (5.7%) | 0 (0%) |
| White | 43 (93.5%) | 25 (89.3%) | 24 (68.6%) | 20 (66.7%) |
| More than one | 3 (6.5%) | 1 (3.6%) | 4 (11.4%) | 3 (10.0%) |
| Other/Missing | 0 (0%) | 2 (7.1%) | 4 (11.4%) | 3 (10.0%) |
| Ethnicity | ||||
| Hispanic | 3 (6.5%) | 0 (0%) | 5 (14.3%) | 6 (20.0%) |
| Non-Hispanic | 42 (91.3%) | 18 (64.3%) | 26 (74.3%) | 21 (70.0%) |
| Prefer not to answer | 0 (0%) | 1 (3.6%) | 0 (0%) | 0 (0%) |
| Missing | 1 (2.2%) | 9 (32.1%) | 4 (11.4%) | 3 (10.0%) |
ggboxplot(BRIEF, x = "LanguageGroup", y = "SES..3.66.",
color = "LanguageGroup",
order = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"),
ylab = "SES", xlab = "Language Group")
ggplot(data=BRIEF, mapping=aes(x=SES..3.66.))+ geom_histogram(binwidth=10) + facet_grid(~LanguageGroup)
leveneTest(SES..3.66.~LanguageGroup, data=BRIEF)
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 2.7556 0.04493 *
## 135
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
SES_LangGrp <- kruskal.test(SES..3.66.~LanguageGroup, data=BRIEF)
SES_LangGrp
##
## Kruskal-Wallis rank sum test
##
## data: SES..3.66. by LanguageGroup
## Kruskal-Wallis chi-squared = 11.094, df = 3, p-value = 0.01123
dunnTest(SES..3.66.~LanguageGroup, data=BRIEF, method="hochberg")
## Dunn (1964) Kruskal-Wallis multiple comparison
## p-values adjusted with the Hochberg method.
## Comparison Z P.unadj P.adj
## 1 Early ASL - Later ASL 1.6920471 0.090637003 0.36254801
## 2 Early ASL - Later English 0.9135500 0.360953336 0.72190667
## 3 Later ASL - Later English -0.8560483 0.391971056 0.39197106
## 4 Early ASL - Typically Hearing -1.1954137 0.231925419 0.69577626
## 5 Later ASL - Typically Hearing -3.1155898 0.001835775 0.01101465
## 6 Later English - Typically Hearing -2.3101242 0.020881279 0.10440639
#selected hochberg correction method referencing this site: https://towardsdatascience.com/an-overview-of-methods-to-address-the-multiple-comparison-problem-310427b3ba92
Findings:
ggboxplot(BRIEF, x = "LanguageGroup", y = "AgeMonths",
color = "LanguageGroup",
order = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"),
ylab = "Age (Months)", xlab = "Language Group")
ggplot(data=BRIEF, mapping=aes(x=AgeMonths))+ geom_histogram(binwidth=6) + facet_grid(~LanguageGroup)
leveneTest(AgeMonths~LanguageGroup, data=BRIEF)
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 1.3757 0.2529
## 135
Age_LangGrp <- kruskal.test(AgeMonths~LanguageGroup, data=BRIEF)
Age_LangGrp
##
## Kruskal-Wallis rank sum test
##
## data: AgeMonths by LanguageGroup
## Kruskal-Wallis chi-squared = 13.74, df = 3, p-value = 0.003281
dunnTest(AgeMonths~LanguageGroup, data=BRIEF, method="hochberg")
## Dunn (1964) Kruskal-Wallis multiple comparison
## p-values adjusted with the Hochberg method.
## Comparison Z P.unadj P.adj
## 1 Early ASL - Later ASL -1.5793242 0.1142617107 0.342785132
## 2 Early ASL - Later English 0.4360839 0.6627758697 0.662775870
## 3 Later ASL - Later English 2.1123433 0.0346570179 0.173285089
## 4 Early ASL - Typically Hearing 1.8632966 0.0624205521 0.249682208
## 5 Later ASL - Typically Hearing 3.6715398 0.0002410935 0.001446561
## 6 Later English - Typically Hearing 1.4982350 0.1340721937 0.268144387
Findings:
table1::label(BRIEF$Hearing_Level_3_Cat) <- "Pre-device hearing level (3 Category)"
table1::label(BRIEF$Hearing.Device) <- "Type of Hearing Device"
table1::label(BRIEF$AoAE) <- "Age of First Auditory Exposure (Months)"
table1::label(BRIEF$Age.of.Language.Exposure..mo.) <- "Age of First Language Exposure (Months)"
table1::table1(~Hearing_Level_3_Cat + Hearing.Device + AoAE + Age.of.Language.Exposure..mo.| LanguageGroup, data = BRIEF, overall=F)
| Typically Hearing (N=46) |
Early ASL (N=28) |
Later English (N=35) |
Later ASL (N=30) |
|
|---|---|---|---|---|
| Pre-device hearing level (3 Category) | ||||
| Typically Hearing | 46 (100%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Deaf | 0 (0%) | 15 (53.6%) | 13 (37.1%) | 21 (70.0%) |
| Hard of Hearing | 0 (0%) | 9 (32.1%) | 21 (60.0%) | 7 (23.3%) |
| Missing | 0 (0%) | 4 (14.3%) | 1 (2.9%) | 2 (6.7%) |
| Type of Hearing Device | ||||
| Hearing Aid | 0 (0%) | 12 (42.9%) | 23 (65.7%) | 13 (43.3%) |
| Cochlear Implant | 0 (0%) | 0 (0%) | 9 (25.7%) | 8 (26.7%) |
| Hearing Aid & Cochlear Implant | 0 (0%) | 0 (0%) | 2 (5.7%) | 1 (3.3%) |
| Other | 0 (0%) | 1 (3.6%) | 1 (2.9%) | 0 (0%) |
| None | 46 (100%) | 15 (53.6%) | 0 (0%) | 8 (26.7%) |
| Age of First Auditory Exposure (Months) | ||||
| Mean (SD) | 0 (0) | 44.1 (27.9) | 16.1 (17.3) | 35.5 (27.5) |
| Median [Min, Max] | 0 [0, 0] | 46.5 [0, 91.0] | 12.0 [0, 58.0] | 34.0 [0, 90.0] |
| Missing | 0 (0%) | 8 (28.6%) | 0 (0%) | 5 (16.7%) |
| Age of First Language Exposure (Months) | ||||
| Mean (SD) | 0 (0) | 0 (0) | 16.2 (17.3) | 41.0 (13.5) |
| Median [Min, Max] | 0 [0, 0] | 0 [0, 0] | 12.0 [0, 58.0] | 36.5 [18.0, 76.0] |
| Missing | 0 (0%) | 0 (0%) | 0 (0%) | 2 (6.7%) |
ggboxplot(BRIEF, x = "LanguageGroup", y = "AoAE",
color = "LanguageGroup",
order = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"),
ylab = "Age of Auditory Exposure (Months)", xlab = "Language Group")
## Warning: Removed 13 rows containing non-finite values (stat_boxplot).
ggplot(data=BRIEF, mapping=aes(x=AoAE))+ geom_histogram(binwidth=15) + facet_grid(~LanguageGroup)
## Warning: Removed 13 rows containing non-finite values (stat_bin).
leveneTest(AoAE~LanguageGroup, data=BRIEF)
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 29.86 1.513e-14 ***
## 122
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
AoAE_LangGrp <- kruskal.test(AoAE~LanguageGroup, data=BRIEF)
AoAE_LangGrp
##
## Kruskal-Wallis rank sum test
##
## data: AoAE by LanguageGroup
## Kruskal-Wallis chi-squared = 75.907, df = 3, p-value = 2.316e-16
dunnTest(AoAE~LanguageGroup, data=BRIEF, method="hochberg")
## Warning: Some rows deleted from 'x' and 'g' because missing data.
## Dunn (1964) Kruskal-Wallis multiple comparison
## p-values adjusted with the Hochberg method.
## Comparison Z P.unadj P.adj
## 1 Early ASL - Later ASL 0.6564261 5.115500e-01 5.115500e-01
## 2 Early ASL - Later English 3.0235127 2.498585e-03 7.495756e-03
## 3 Later ASL - Later English 2.4844464 1.297531e-02 2.595061e-02
## 4 Early ASL - Typically Hearing 7.1732046 7.326221e-13 4.395733e-12
## 5 Later ASL - Typically Hearing 6.9397910 3.926804e-12 1.963402e-11
## 6 Later English - Typically Hearing 4.7872196 1.691078e-06 6.764310e-06
Findings:
ggboxplot(BRIEF, x = "LanguageGroup", y = "Age.of.Language.Exposure..mo.",
color = "LanguageGroup",
order = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"),
ylab = "Age of Language Exposure (Months)", xlab = "Language Group")
## Warning: Removed 2 rows containing non-finite values (stat_boxplot).
ggplot(data=BRIEF, mapping=aes(x=Age.of.Language.Exposure..mo.))+ geom_histogram(binwidth=10) + facet_grid(~LanguageGroup)
## Warning: Removed 2 rows containing non-finite values (stat_bin).
leveneTest(Age.of.Language.Exposure..mo.~LanguageGroup, data=BRIEF)
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 34.088 < 2.2e-16 ***
## 133
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
AoLE_LangGrp <- kruskal.test(Age.of.Language.Exposure..mo.~LanguageGroup, data=BRIEF)
AoLE_LangGrp
##
## Kruskal-Wallis rank sum test
##
## data: Age.of.Language.Exposure..mo. by LanguageGroup
## Kruskal-Wallis chi-squared = 107.38, df = 3, p-value < 2.2e-16
dunnTest(Age.of.Language.Exposure..mo.~LanguageGroup, data=BRIEF, method="hochberg")
## Warning: Some rows deleted from 'x' and 'g' because missing data.
## Dunn (1964) Kruskal-Wallis multiple comparison
## p-values adjusted with the Hochberg method.
## Comparison Z P.unadj P.adj
## 1 Early ASL - Later ASL -8.173761 2.989226e-16 1.494613e-15
## 2 Early ASL - Later English -4.861165 1.166972e-06 3.500916e-06
## 3 Later ASL - Later English 3.754736 1.735245e-04 3.470490e-04
## 4 Early ASL - Typically Hearing 0.000000 1.000000e+00 1.000000e+00
## 5 Later ASL - Typically Hearing 9.113810 7.953855e-20 4.772313e-19
## 6 Later English - Typically Hearing 5.495004 3.907022e-08 1.562809e-07
Findings:
Approach: Compare (via Welch’s t-tests) the BRIEF raw scores of Early English group to those of Early ASL group
Create dataframe with only children exposed to language early and perform t-tests for all BRIEF-P scales
BRIEF_early <- subset(BRIEF_AgeOf, BRIEF_AgeOf$Language_Timing=="Early")
BRIEF Scores & Welch two sample t-tests for two “Early” groups
table1::label(BRIEF_early$GEC_RawScore) <- "Global Executive Composite"
table1::label(BRIEF_early$Inhibit_RawScore) <- "Inhibition"
table1::label(BRIEF_early$Shift_RawScore) <- "Shift"
table1::label(BRIEF_early$Emotional.Control_RawScore) <- "Emotional Control"
table1::label(BRIEF_early$Working.Memory_RawScore) <- "Working Memory"
table1::label(BRIEF_early$Plan.Organize_RawScore) <- "Plan/Organize"
table1(~GEC_RawScore + Inhibit_RawScore + Shift_RawScore + Emotional.Control_RawScore + Working.Memory_RawScore + Plan.Organize_RawScore | Language_Modality, data = BRIEF_early, overall=F)
| English (N=46) |
ASL (N=20) |
|
|---|---|---|
| Global Executive Composite | ||
| Mean (SD) | 87.8 (18.7) | 85.6 (13.5) |
| Median [Min, Max] | 87.5 [63.0, 155] | 87.5 [63.0, 103] |
| Inhibition | ||
| Mean (SD) | 22.8 (5.93) | 22.9 (5.26) |
| Median [Min, Max] | 23.0 [16.0, 46.0] | 22.0 [16.0, 34.0] |
| Shift | ||
| Mean (SD) | 13.0 (3.11) | 13.0 (2.67) |
| Median [Min, Max] | 12.0 [10.0, 24.0] | 12.5 [10.0, 17.0] |
| Emotional Control | ||
| Mean (SD) | 15.0 (3.67) | 14.5 (2.78) |
| Median [Min, Max] | 14.0 [10.0, 24.0] | 14.0 [10.0, 19.0] |
| Working Memory | ||
| Mean (SD) | 22.7 (6.61) | 22.0 (4.50) |
| Median [Min, Max] | 20.5 [17.0, 48.0] | 21.0 [17.0, 31.0] |
| Plan/Organize | ||
| Mean (SD) | 14.3 (3.40) | 13.4 (2.52) |
| Median [Min, Max] | 13.5 [10.0, 23.0] | 13.0 [10.0, 18.0] |
t.test(BRIEF_early$GEC_RawScore~BRIEF_early$Language_Modality)
##
## Welch Two Sample t-test
##
## data: BRIEF_early$GEC_RawScore by BRIEF_early$Language_Modality
## t = 0.53481, df = 49.488, p-value = 0.5952
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -6.016656 10.381874
## sample estimates:
## mean in group English mean in group ASL
## 87.78261 85.60000
t.test(BRIEF_early$Inhibit_RawScore~BRIEF_early$Language_Modality)
##
## Welch Two Sample t-test
##
## data: BRIEF_early$Inhibit_RawScore by BRIEF_early$Language_Modality
## t = -0.060772, df = 40.562, p-value = 0.9518
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.052056 2.873796
## sample estimates:
## mean in group English mean in group ASL
## 22.76087 22.85000
t.test(BRIEF_early$Shift_RawScore~BRIEF_early$Language_Modality)
##
## Welch Two Sample t-test
##
## data: BRIEF_early$Shift_RawScore by BRIEF_early$Language_Modality
## t = 0.095418, df = 41.938, p-value = 0.9244
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.445606 1.589084
## sample estimates:
## mean in group English mean in group ASL
## 13.02174 12.95000
t.test(BRIEF_early$Emotional.Control_RawScore~BRIEF_early$Language_Modality)
##
## Welch Two Sample t-test
##
## data: BRIEF_early$Emotional.Control_RawScore by BRIEF_early$Language_Modality
## t = 0.64064, df = 47.264, p-value = 0.5249
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.130341 2.186863
## sample estimates:
## mean in group English mean in group ASL
## 14.97826 14.45000
t.test(BRIEF_early$Working.Memory_RawScore~BRIEF_early$Language_Modality)
##
## Welch Two Sample t-test
##
## data: BRIEF_early$Working.Memory_RawScore by BRIEF_early$Language_Modality
## t = 0.51255, df = 52.046, p-value = 0.6104
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.091134 3.525916
## sample estimates:
## mean in group English mean in group ASL
## 22.71739 22.00000
t.test(BRIEF_early$Plan.Organize_RawScore~BRIEF_early$Language_Modality)
##
## Welch Two Sample t-test
##
## data: BRIEF_early$Plan.Organize_RawScore by BRIEF_early$Language_Modality
## t = 1.2659, df = 48.223, p-value = 0.2116
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.5612263 2.4699219
## sample estimates:
## mean in group English mean in group ASL
## 14.30435 13.35000
Findings: Early ASL group did not significantly differ from Typically Hearing group on any subscale ***
Approach: Linear regression models for the BRIEF-P raw scores on general composite score and each subscale predicted by age of auditory and age of language exposure, and other demographic characteristics (SES, sex, age)
Also testing whether other demographic variables or ways of characterizing group differences (Length of Auditory Exposure, Language Modality) are better predictive of BRIEF scores
## Function below from: http://www.sthda.com/english/wiki/correlation-matrix-formatting-and-visualization
num_predictors <- BRIEF_AgeOf[, c("SES..3.66.", "AgeMonths", "Age.of.Language.Exposure..mo.", "AoAE")]
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
row = rownames(cormat)[row(cormat)[ut]],
column = rownames(cormat)[col(cormat)[ut]],
cor =(cormat)[ut],
p = pmat[ut]
)
}
BRIEF_num_pred <- rcorr(as.matrix(num_predictors), type = c("pearson"))
flattenCorrMatrix(BRIEF_num_pred$r, BRIEF_num_pred$P)
## row column cor
## 1 SES..3.66. AgeMonths 0.006453835
## 2 SES..3.66. Age.of.Language.Exposure..mo. -0.165108899
## 3 AgeMonths Age.of.Language.Exposure..mo. 0.397791343
## 4 SES..3.66. AoAE -0.152264510
## 5 AgeMonths AoAE 0.400178811
## 6 Age.of.Language.Exposure..mo. AoAE 0.452745947
## p
## 1 9.430540e-01
## 2 6.575670e-02
## 3 4.352450e-06
## 4 9.004399e-02
## 5 3.765614e-06
## 6 1.150699e-07
Findings:
Step 1. Base Model (Demographic variables only):
overall_all_base <- lm(data=BRIEF_AgeOf, formula = GEC_RawScore~SES..3.66.+Sex+AgeMonths)
summary(overall_all_base)
##
## Call:
## lm(formula = GEC_RawScore ~ SES..3.66. + Sex + AgeMonths, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -49.301 -16.784 -0.489 10.601 60.607
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 96.5244 11.0764 8.714 1.83e-14 ***
## SES..3.66. -0.3029 0.1283 -2.360 0.0199 *
## SexMale -0.6580 3.6146 -0.182 0.8559
## AgeMonths 0.1772 0.1393 1.272 0.2057
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.83 on 121 degrees of freedom
## Multiple R-squared: 0.05657, Adjusted R-squared: 0.03318
## F-statistic: 2.419 on 3 and 121 DF, p-value: 0.06954
AIC(overall_all_base)
## [1] 1107.494
Step 2. Add Age of Language Exposure:
overall_all_AoLE <- lm(data=BRIEF_AgeOf, formula = GEC_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.)
summary(overall_all_AoLE)
##
## Call:
## lm(formula = GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.,
## data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53.541 -14.523 -1.440 9.773 66.360
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 101.05856 10.78623 9.369 5.47e-16 ***
## SES..3.66. -0.23515 0.12571 -1.871 0.06384 .
## SexMale -1.91594 3.51118 -0.546 0.58631
## AgeMonths -0.01617 0.14781 -0.109 0.91308
## Age.of.Language.Exposure..mo. 0.32768 0.10407 3.149 0.00207 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.14 on 120 degrees of freedom
## Multiple R-squared: 0.1286, Adjusted R-squared: 0.09952
## F-statistic: 4.426 on 4 and 120 DF, p-value: 0.002261
AIC(overall_all_AoLE)
## [1] 1099.571
Compare Model from Step 2 to model from Step 1:
anova(overall_all_base, overall_all_AoLE)
## Analysis of Variance Table
##
## Model 1: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 121 47591
## 2 120 43959 1 3631.8 9.9142 0.00207 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
3a. Add Age of Auditory Exposure:
overall_all_AoLE_AoAE <- lm(data=BRIEF_AgeOf, formula = GEC_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+AoAE)
summary(overall_all_AoLE_AoAE)
##
## Call:
## lm(formula = GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. +
## AoAE, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -54.475 -13.271 0.398 10.333 65.083
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 100.35055 10.83174 9.264 1.03e-15 ***
## SES..3.66. -0.24696 0.12664 -1.950 0.05351 .
## SexMale -1.80034 3.51807 -0.512 0.60978
## AgeMonths 0.02029 0.15417 0.132 0.89550
## Age.of.Language.Exposure..mo. 0.35769 0.11010 3.249 0.00151 **
## AoAE -0.06959 0.08247 -0.844 0.40045
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.16 on 119 degrees of freedom
## Multiple R-squared: 0.1338, Adjusted R-squared: 0.09735
## F-statistic: 3.675 on 5 and 119 DF, p-value: 0.003968
AIC(overall_all_AoLE_AoAE)
## [1] 1100.826
3b. Add Length of Auditory Experience:
overall_all_AoLE_LoAE <- lm(data=BRIEF_AgeOf, formula = GEC_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Length.of.Auditory.Exposure)
summary(overall_all_AoLE_LoAE)
##
## Call:
## lm(formula = GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. +
## Length.of.Auditory.Exposure, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -54.333 -13.081 0.011 10.348 65.447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 100.55746 10.83983 9.277 9.67e-16 ***
## SES..3.66. -0.24285 0.12657 -1.919 0.05742 .
## SexMale -1.79263 3.52479 -0.509 0.61199
## AgeMonths -0.04401 0.15425 -0.285 0.77589
## Age.of.Language.Exposure..mo. 0.35299 0.11136 3.170 0.00194 **
## Length.of.Auditory.Exposure 0.05363 0.08260 0.649 0.51739
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.19 on 119 degrees of freedom
## Multiple R-squared: 0.1316, Adjusted R-squared: 0.09516
## F-statistic: 3.608 on 5 and 119 DF, p-value: 0.004489
AIC(overall_all_AoLE_LoAE)
## [1] 1101.129
3c. Add Modality:
overall_all_AoLE_Modality <- lm(data=BRIEF_AgeOf, formula = GEC_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Language_Modality)
summary(overall_all_AoLE_Modality)
##
## Call:
## lm(formula = GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. +
## Language_Modality, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.021 -13.485 -0.447 10.692 65.208
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.015e+02 1.080e+01 9.399 4.97e-16 ***
## SES..3.66. -2.458e-01 1.263e-01 -1.947 0.05392 .
## SexMale -1.885e+00 3.513e+00 -0.537 0.59259
## AgeMonths 9.928e-04 1.490e-01 0.007 0.99469
## Age.of.Language.Exposure..mo. 3.582e-01 1.089e-01 3.289 0.00132 **
## Language_ModalityASL -3.731e+00 3.915e+00 -0.953 0.34253
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.15 on 119 degrees of freedom
## Multiple R-squared: 0.1352, Adjusted R-squared: 0.09883
## F-statistic: 3.72 on 5 and 119 DF, p-value: 0.00365
AIC(overall_all_AoLE_Modality)
## [1] 1100.621
3d.Add Hearing Status (3-category):
overall_all_AoLE_HearStat <- lm(data=BRIEF_AgeOf, formula = GEC_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+ Hearing_Level_3_Cat)
summary(overall_all_AoLE_HearStat)
##
## Call:
## lm(formula = GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. +
## Hearing_Level_3_Cat, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53.778 -14.321 -0.354 10.311 65.300
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102.69671 11.90918 8.623 4.86e-14 ***
## SES..3.66. -0.23675 0.13238 -1.788 0.07641 .
## SexMale -0.98225 3.64774 -0.269 0.78821
## AgeMonths -0.02332 0.16169 -0.144 0.88559
## Age.of.Language.Exposure..mo. 0.32526 0.12297 2.645 0.00934 **
## Hearing_Level_3_CatDeaf -2.47951 5.07363 -0.489 0.62601
## Hearing_Level_3_CatHard of Hearing -1.86876 4.95996 -0.377 0.70706
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.35 on 112 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.1094, Adjusted R-squared: 0.06172
## F-statistic: 2.294 on 6 and 112 DF, p-value: 0.03985
AIC(overall_all_AoLE_HearStat)
## [1] 1051.63
Compare Models from Step 3a-c to model from Step 2:
anova(overall_all_AoLE, overall_all_AoLE_AoAE)
## Analysis of Variance Table
##
## Model 1: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. +
## AoAE
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 43959
## 2 119 43697 1 261.48 0.7121 0.4005
anova(overall_all_AoLE, overall_all_AoLE_Modality)
## Analysis of Variance Table
##
## Model 1: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. +
## Language_Modality
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 43959
## 2 119 43626 1 332.94 0.9082 0.3425
anova(overall_all_AoLE, overall_all_AoLE_LoAE)
## Analysis of Variance Table
##
## Model 1: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. +
## Length.of.Auditory.Exposure
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 43959
## 2 119 43804 1 155.19 0.4216 0.5174
Findings: Age of auditory exposure, length of auditory exposure, and language modality are not significant predictors of GEC raw scores, and their addition does not improve model fit. We cannot do a direct comparison of model in step 3d to the model in step 2 because we are missing hearing status information for 12 participants (mostly Early and Later ASL). However, even in the model in step 3d, Hearing Status is not a significant predictor of BRIEF scores, and Age of Language Exposure is.
Checking assumptions for best-fit GEC model
ols_plot_resid_qq(overall_all_AoLE)
ols_test_normality(overall_all_AoLE)
## -----------------------------------------------
## Test Statistic pvalue
## -----------------------------------------------
## Shapiro-Wilk 0.9802 0.0638
## Kolmogorov-Smirnov 0.0657 0.6533
## Cramer-von Mises 9.9712 0.0000
## Anderson-Darling 0.7339 0.0544
## -----------------------------------------------
ols_plot_resid_hist(overall_all_AoLE)
ols_test_correlation(overall_all_AoLE)
## [1] 0.9882637
ols_plot_resid_fit(overall_all_AoLE)
Step 1. Base Model:
inhibition_all_base <- lm(data=BRIEF_AgeOf, formula = Inhibit_RawScore~SES..3.66.+Sex+AgeMonths)
summary(inhibition_all_base)
##
## Call:
## lm(formula = Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths,
## data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.3759 -4.6435 -0.0437 4.4091 21.5108
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.35171 3.52670 6.338 4.16e-09 ***
## SES..3.66. -0.08194 0.04086 -2.005 0.0472 *
## SexMale 0.82837 1.15087 0.720 0.4730
## AgeMonths 0.08672 0.04436 1.955 0.0529 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.314 on 121 degrees of freedom
## Multiple R-squared: 0.06463, Adjusted R-squared: 0.04144
## F-statistic: 2.787 on 3 and 121 DF, p-value: 0.04365
AIC(inhibition_all_base)
## [1] 821.3795
Step 2. Add Age of Language Exposure:
inhibition_all_AoLE <- lm(data=BRIEF_AgeOf, formula = Inhibit_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.)
summary(inhibition_all_AoLE)
##
## Call:
## lm(formula = Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.2479 -4.3006 -0.4799 4.2384 22.6940
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.28421 3.51600 6.622 1.05e-09 ***
## SES..3.66. -0.06801 0.04098 -1.660 0.0996 .
## SexMale 0.56967 1.14454 0.498 0.6196
## AgeMonths 0.04695 0.04818 0.974 0.3318
## Age.of.Language.Exposure..mo. 0.06739 0.03392 1.987 0.0492 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.239 on 120 degrees of freedom
## Multiple R-squared: 0.09441, Adjusted R-squared: 0.06422
## F-statistic: 3.128 on 4 and 120 DF, p-value: 0.01737
AIC(inhibition_all_AoLE)
## [1] 819.3348
Compare Model with Age of Language Exposure to base demographic model:
anova(inhibition_all_base, inhibition_all_AoLE)
## Analysis of Variance Table
##
## Model 1: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 121 4824.5
## 2 120 4670.9 1 153.61 3.9464 0.04925 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
3a. Add Age of Auditory Experience:
inhibition_all_AoLE_AoAE <- lm(data=BRIEF_AgeOf, formula = Inhibit_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+AoAE)
summary(inhibition_all_AoLE_AoAE)
##
## Call:
## lm(formula = Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo. + AoAE, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.4387 -4.1619 -0.5115 4.2057 22.4330
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.13959 3.53725 6.542 1.6e-09 ***
## SES..3.66. -0.07042 0.04135 -1.703 0.0912 .
## SexMale 0.59328 1.14887 0.516 0.6065
## AgeMonths 0.05440 0.05035 1.080 0.2821
## Age.of.Language.Exposure..mo. 0.07352 0.03595 2.045 0.0431 *
## AoAE -0.01421 0.02693 -0.528 0.5986
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.258 on 119 degrees of freedom
## Multiple R-squared: 0.09652, Adjusted R-squared: 0.05856
## F-statistic: 2.543 on 5 and 119 DF, p-value: 0.03178
AIC(inhibition_all_AoLE_AoAE)
## [1] 821.0425
3b. Add Length of Auditory Exposure:
inhibition_all_AoLE_LoAE <- lm(data=BRIEF_AgeOf, formula = Inhibit_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Length.of.Auditory.Exposure)
summary(inhibition_all_AoLE_LoAE)
##
## Call:
## lm(formula = Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo. + Length.of.Auditory.Exposure,
## data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.3982 -4.1459 -0.5254 4.3701 22.5208
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.18916 3.53761 6.555 1.5e-09 ***
## SES..3.66. -0.06947 0.04131 -1.682 0.0952 .
## SexMale 0.59306 1.15033 0.516 0.6071
## AgeMonths 0.04167 0.05034 0.828 0.4095
## Age.of.Language.Exposure..mo. 0.07219 0.03634 1.986 0.0493 *
## Length.of.Auditory.Exposure 0.01017 0.02696 0.377 0.7065
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.261 on 119 degrees of freedom
## Multiple R-squared: 0.09549, Adjusted R-squared: 0.05749
## F-statistic: 2.513 on 5 and 119 DF, p-value: 0.03355
AIC(inhibition_all_AoLE_LoAE)
## [1] 821.1852
3c. Add Modality:
inhibition_all_AoLE_Modality <- lm(data=BRIEF_AgeOf, formula = Inhibit_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+ Language_Modality)
summary(inhibition_all_AoLE_Modality)
##
## Call:
## lm(formula = Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo. + Language_Modality, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.739 -4.417 -0.699 4.137 22.461
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.37747 3.52915 6.624 1.07e-09 ***
## SES..3.66. -0.07016 0.04125 -1.701 0.0916 .
## SexMale 0.57598 1.14772 0.502 0.6167
## AgeMonths 0.05042 0.04867 1.036 0.3024
## Age.of.Language.Exposure..mo. 0.07357 0.03559 2.067 0.0409 *
## Language_ModalityASL -0.75393 1.27919 -0.589 0.5567
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.256 on 119 degrees of freedom
## Multiple R-squared: 0.09704, Adjusted R-squared: 0.05911
## F-statistic: 2.558 on 5 and 119 DF, p-value: 0.03091
AIC(inhibition_all_AoLE_Modality)
## [1] 820.9704
3d. Add Hearing Status (3-category):
inhibition_all_AoLE_HearStat <- lm(data=BRIEF_AgeOf, formula = Inhibit_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+ Hearing_Level_3_Cat)
summary(inhibition_all_AoLE_HearStat)
##
## Call:
## lm(formula = Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo. + Hearing_Level_3_Cat, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.0506 -4.3309 -0.0461 4.0739 22.5856
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.27720 3.84904 6.307 5.82e-09 ***
## SES..3.66. -0.06467 0.04278 -1.511 0.133
## SexMale 0.68541 1.17895 0.581 0.562
## AgeMonths 0.03202 0.05226 0.613 0.541
## Age.of.Language.Exposure..mo. 0.05377 0.03974 1.353 0.179
## Hearing_Level_3_CatDeaf 0.48590 1.63980 0.296 0.768
## Hearing_Level_3_CatHard of Hearing -1.56960 1.60306 -0.979 0.330
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.254 on 112 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.09152, Adjusted R-squared: 0.04285
## F-statistic: 1.881 on 6 and 112 DF, p-value: 0.09026
AIC(inhibition_all_AoLE_HearStat)
## [1] 782.8125
Compare Models from Step 3a-c to model from Step 2:
anova(inhibition_all_AoLE, inhibition_all_AoLE_AoAE)
## Analysis of Variance Table
##
## Model 1: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. +
## AoAE
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 4670.9
## 2 119 4660.0 1 10.91 0.2786 0.5986
anova(inhibition_all_AoLE, inhibition_all_AoLE_Modality)
## Analysis of Variance Table
##
## Model 1: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. +
## Language_Modality
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 4670.9
## 2 119 4657.3 1 13.595 0.3474 0.5567
anova(inhibition_all_AoLE, inhibition_all_AoLE_LoAE)
## Analysis of Variance Table
##
## Model 1: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. +
## Length.of.Auditory.Exposure
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 4670.9
## 2 119 4665.4 1 5.584 0.1424 0.7065
Checking assumptions for best-fit Inhibition model
ols_plot_resid_qq(inhibition_all_AoLE)
ols_test_normality(inhibition_all_AoLE)
## -----------------------------------------------
## Test Statistic pvalue
## -----------------------------------------------
## Shapiro-Wilk 0.9772 0.0330
## Kolmogorov-Smirnov 0.0813 0.3807
## Cramer-von Mises 9.7914 0.0000
## Anderson-Darling 0.683 0.0727
## -----------------------------------------------
ols_plot_resid_hist(inhibition_all_AoLE)
ols_test_correlation(inhibition_all_AoLE)
## [1] 0.9853621
ols_plot_resid_fit(inhibition_all_AoLE)
Step 1. Base Model:
shift_all_base <- lm(data=BRIEF_AgeOf, formula = Shift_RawScore~SES..3.66.+Sex+AgeMonths)
summary(shift_all_base)
##
## Call:
## lm(formula = Shift_RawScore ~ SES..3.66. + Sex + AgeMonths, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.4632 -2.8668 -0.6954 2.0394 15.2974
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.752518 2.046755 8.185 3.17e-13 ***
## SES..3.66. -0.060132 0.023714 -2.536 0.0125 *
## SexMale -0.234463 0.667921 -0.351 0.7262
## AgeMonths 0.008393 0.025743 0.326 0.7450
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.665 on 121 degrees of freedom
## Multiple R-squared: 0.05149, Adjusted R-squared: 0.02797
## F-statistic: 2.189 on 3 and 121 DF, p-value: 0.09279
AIC(shift_all_base)
## [1] 685.3529
Step 2. Add Age of Language Exposure:
shift_all_AoLE <- lm(data=BRIEF_AgeOf, formula = Shift_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.)
summary(shift_all_AoLE)
##
## Call:
## lm(formula = Shift_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.9782 -2.4040 -0.3517 1.9774 14.0519
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.70857 1.96810 8.998 4.15e-15 ***
## SES..3.66. -0.04585 0.02294 -1.999 0.047874 *
## SexMale -0.49970 0.64067 -0.780 0.436942
## AgeMonths -0.03239 0.02697 -1.201 0.232180
## Age.of.Language.Exposure..mo. 0.06909 0.01899 3.639 0.000405 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.492 on 120 degrees of freedom
## Multiple R-squared: 0.1457, Adjusted R-squared: 0.1173
## F-statistic: 5.118 on 4 and 120 DF, p-value: 0.0007661
AIC(shift_all_AoLE)
## [1] 674.271
Compare Model with Age of Language Exposure to base demographic model:
anova(shift_all_base, shift_all_AoLE)
## Analysis of Variance Table
##
## Model 1: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 121 1625.0
## 2 120 1463.5 1 161.47 13.239 0.0004055 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
3a. Add Age of Auditory Experience:
shift_all_AoLE_AoAE <- lm(data=BRIEF_AgeOf, formula = Shift_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+AoAE)
summary(shift_all_AoLE_AoAE)
##
## Call:
## lm(formula = Shift_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo. + AoAE, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.8804 -2.3444 -0.3614 2.1039 14.0925
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.672586 1.981853 8.917 6.81e-15 ***
## SES..3.66. -0.046451 0.023170 -2.005 0.047258 *
## SexMale -0.493827 0.643691 -0.767 0.444494
## AgeMonths -0.030535 0.028208 -1.082 0.281235
## Age.of.Language.Exposure..mo. 0.070618 0.020144 3.506 0.000643 ***
## AoAE -0.003536 0.015088 -0.234 0.815094
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.506 on 119 degrees of freedom
## Multiple R-squared: 0.1461, Adjusted R-squared: 0.1103
## F-statistic: 4.073 on 5 and 119 DF, p-value: 0.001896
AIC(shift_all_AoLE_AoAE)
## [1] 676.2133
3b. Add Length of Auditory Exposure:
shift_all_AoLE_LoAE <- lm(data=BRIEF_AgeOf, formula = Shift_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Length.of.Auditory.Exposure)
summary(shift_all_AoLE_LoAE)
##
## Call:
## lm(formula = Shift_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo. + Length.of.Auditory.Exposure,
## data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.903 -2.339 -0.355 2.083 14.085
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.681350 1.981072 8.925 6.52e-15 ***
## SES..3.66. -0.046269 0.023132 -2.000 0.047752 *
## SexMale -0.493004 0.644186 -0.765 0.445601
## AgeMonths -0.033900 0.028191 -1.203 0.231554
## Age.of.Language.Exposure..mo. 0.070468 0.020352 3.462 0.000745 ***
## Length.of.Auditory.Exposure 0.002913 0.015096 0.193 0.847317
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.506 on 119 degrees of freedom
## Multiple R-squared: 0.146, Adjusted R-squared: 0.1101
## F-statistic: 4.069 on 5 and 119 DF, p-value: 0.001911
AIC(shift_all_AoLE_LoAE)
## [1] 676.2319
3c. Add Modality:
shift_all_AoLE_Modality <- lm(data=BRIEF_AgeOf, formula = Shift_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Language_Modality)
summary(shift_all_AoLE_Modality)
##
## Call:
## lm(formula = Shift_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo. + Language_Modality, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.9964 -2.4716 -0.3886 1.9101 14.0187
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.69751 1.97821 8.946 5.82e-15 ***
## SES..3.66. -0.04560 0.02312 -1.972 0.05094 .
## SexMale -0.50045 0.64334 -0.778 0.43817
## AgeMonths -0.03280 0.02728 -1.202 0.23167
## Age.of.Language.Exposure..mo. 0.06836 0.01995 3.426 0.00084 ***
## Language_ModalityASL 0.08940 0.71703 0.125 0.90099
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.507 on 119 degrees of freedom
## Multiple R-squared: 0.1458, Adjusted R-squared: 0.11
## F-statistic: 4.064 on 5 and 119 DF, p-value: 0.001929
AIC(shift_all_AoLE_Modality)
## [1] 676.2547
3d. Add Hearing Status (3-category):
shift_all_AoLE_HearStat <- lm(data=BRIEF_AgeOf, formula = Shift_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+ Hearing_Level_3_Cat)
summary(shift_all_AoLE_HearStat)
##
## Call:
## lm(formula = Shift_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo. + Hearing_Level_3_Cat, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1751 -2.5515 -0.1856 1.7464 13.0271
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.15085 2.14013 8.014 1.16e-12 ***
## SES..3.66. -0.04774 0.02379 -2.007 0.04718 *
## SexMale -0.24433 0.65551 -0.373 0.71005
## AgeMonths -0.02479 0.02906 -0.853 0.39538
## Age.of.Language.Exposure..mo. 0.05993 0.02210 2.712 0.00775 **
## Hearing_Level_3_CatDeaf -0.25406 0.91175 -0.279 0.78103
## Hearing_Level_3_CatHard of Hearing 1.46543 0.89133 1.644 0.10296
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.478 on 112 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.1649, Adjusted R-squared: 0.1202
## F-statistic: 3.687 on 6 and 112 DF, p-value: 0.002216
AIC(shift_all_AoLE_HearStat)
## [1] 643.1168
Compare Models from Step 3a-c to model from Step 2:
anova(shift_all_AoLE, shift_all_AoLE_AoAE)
## Analysis of Variance Table
##
## Model 1: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. +
## AoAE
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 1463.5
## 2 119 1462.8 1 0.67529 0.0549 0.8151
anova(shift_all_AoLE, shift_all_AoLE_Modality)
## Analysis of Variance Table
##
## Model 1: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. +
## Language_Modality
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 1463.5
## 2 119 1463.3 1 0.19114 0.0155 0.901
anova(shift_all_AoLE, shift_all_AoLE_LoAE)
## Analysis of Variance Table
##
## Model 1: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. +
## Length.of.Auditory.Exposure
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 1463.5
## 2 119 1463.1 1 0.45779 0.0372 0.8473
Checking assumptions for best-fit Shift model
ols_plot_resid_qq(shift_all_AoLE)
ols_test_normality(shift_all_AoLE)
## -----------------------------------------------
## Test Statistic pvalue
## -----------------------------------------------
## Shapiro-Wilk 0.938 0.0000
## Kolmogorov-Smirnov 0.0833 0.3509
## Cramer-von Mises 10.2889 0.0000
## Anderson-Darling 1.6493 3e-04
## -----------------------------------------------
ols_plot_resid_hist(shift_all_AoLE)
ols_test_correlation(shift_all_AoLE)
## [1] 0.9667448
ols_plot_resid_fit(shift_all_AoLE)
#### Emotional Control Models Step 1. Base Model:
emotctrl_all_base <- lm(data=BRIEF_AgeOf, formula = Emotional.Control_RawScore~SES..3.66.+Sex+AgeMonths)
summary(emotctrl_all_base)
##
## Call:
## lm(formula = Emotional.Control_RawScore ~ SES..3.66. + Sex +
## AgeMonths, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1533 -3.0615 -0.6865 2.3701 14.1351
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.43032 2.24218 7.774 2.8e-12 ***
## SES..3.66. -0.06219 0.02598 -2.394 0.0182 *
## SexMale -0.39127 0.73170 -0.535 0.5938
## AgeMonths 0.01649 0.02820 0.585 0.5597
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.015 on 121 degrees of freedom
## Multiple R-squared: 0.049, Adjusted R-squared: 0.02542
## F-statistic: 2.078 on 3 and 121 DF, p-value: 0.1066
Step 2. Add Age of Language Exposure:
emotctrl_all_AoLE <- lm(data=BRIEF_AgeOf, formula = Emotional.Control_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.)
summary(emotctrl_all_AoLE)
##
## Call:
## lm(formula = Emotional.Control_RawScore ~ SES..3.66. + Sex +
## AgeMonths + Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.0073 -2.8455 -0.6204 2.6420 13.5066
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.912837 2.247759 7.969 1.04e-12 ***
## SES..3.66. -0.054985 0.026197 -2.099 0.0379 *
## SexMale -0.525139 0.731700 -0.718 0.4743
## AgeMonths -0.004089 0.030803 -0.133 0.8946
## Age.of.Language.Exposure..mo. 0.034871 0.021687 1.608 0.1105
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.989 on 120 degrees of freedom
## Multiple R-squared: 0.06906, Adjusted R-squared: 0.03803
## F-statistic: 2.225 on 4 and 120 DF, p-value: 0.07024
Compare Model with Age of Language Exposure to base demographic model:
anova(emotctrl_all_base, emotctrl_all_AoLE)
## Analysis of Variance Table
##
## Model 1: Emotional.Control_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Emotional.Control_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 121 1950.1
## 2 120 1909.0 1 41.129 2.5854 0.1105
3a. Add Age of Auditory Experience:
emotctrl_all_AoLE_AoAE <- lm(data=BRIEF_AgeOf, formula = Emotional.Control_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+AoAE)
summary(emotctrl_all_AoLE_AoAE)
##
## Call:
## lm(formula = Emotional.Control_RawScore ~ SES..3.66. + Sex +
## AgeMonths + Age.of.Language.Exposure..mo. + AoAE, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1383 -2.8960 -0.6379 2.6331 13.6172
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.8147979 2.2610112 7.879 1.74e-12 ***
## SES..3.66. -0.0566203 0.0264338 -2.142 0.0342 *
## SexMale -0.5091322 0.7343598 -0.693 0.4895
## AgeMonths 0.0009605 0.0321817 0.030 0.9762
## Age.of.Language.Exposure..mo. 0.0390263 0.0229814 1.698 0.0921 .
## AoAE -0.0096359 0.0172137 -0.560 0.5767
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4 on 119 degrees of freedom
## Multiple R-squared: 0.0715, Adjusted R-squared: 0.03249
## F-statistic: 1.833 on 5 and 119 DF, p-value: 0.1115
3b. Add Length of Auditory Experience:
emotctrl_all_AoLE_LoAE <- lm(data=BRIEF_AgeOf, formula = Emotional.Control_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Length.of.Auditory.Exposure)
summary(emotctrl_all_AoLE_LoAE)
##
## Call:
## lm(formula = Emotional.Control_RawScore ~ SES..3.66. + Sex +
## AgeMonths + Age.of.Language.Exposure..mo. + Length.of.Auditory.Exposure,
## data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1219 -2.8985 -0.6491 2.6069 13.6073
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.829274 2.260367 7.888 1.66e-12 ***
## SES..3.66. -0.056269 0.026393 -2.132 0.0351 *
## SexMale -0.504576 0.735005 -0.686 0.4937
## AgeMonths -0.008732 0.032165 -0.271 0.7865
## Age.of.Language.Exposure..mo. 0.039090 0.023222 1.683 0.0949 .
## Length.of.Auditory.Exposure 0.008944 0.017224 0.519 0.6045
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.001 on 119 degrees of freedom
## Multiple R-squared: 0.07116, Adjusted R-squared: 0.03214
## F-statistic: 1.823 on 5 and 119 DF, p-value: 0.1134
3c. Add Modality:
emotctrl_all_AoLE_Modality <- lm(data=BRIEF_AgeOf, formula = Emotional.Control_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Language_Modality)
summary(emotctrl_all_AoLE_Modality)
##
## Call:
## lm(formula = Emotional.Control_RawScore ~ SES..3.66. + Sex +
## AgeMonths + Age.of.Language.Exposure..mo. + Language_Modality,
## data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.0074 -2.8456 -0.6204 2.6421 13.5066
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.9128491 2.2594559 7.928 1.35e-12 ***
## SES..3.66. -0.0549852 0.0264098 -2.082 0.0395 *
## SexMale -0.5251381 0.7348004 -0.715 0.4762
## AgeMonths -0.0040882 0.0311610 -0.131 0.8958
## Age.of.Language.Exposure..mo. 0.0348719 0.0227881 1.530 0.1286
## Language_ModalityASL -0.0001008 0.8189740 0.000 0.9999
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.005 on 119 degrees of freedom
## Multiple R-squared: 0.06906, Adjusted R-squared: 0.02994
## F-statistic: 1.766 on 5 and 119 DF, p-value: 0.1251
3d. Add Hearing Status (3-category):
emotctrl_all_AoLE_HearStat <- lm(data=BRIEF_AgeOf, formula = Emotional.Control_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+ Hearing_Level_3_Cat)
summary(emotctrl_all_AoLE_HearStat)
##
## Call:
## lm(formula = Emotional.Control_RawScore ~ SES..3.66. + Sex +
## AgeMonths + Age.of.Language.Exposure..mo. + Hearing_Level_3_Cat,
## data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.7606 -2.6841 -0.5356 2.8101 13.5574
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.69331 2.46357 7.588 1.04e-11 ***
## SES..3.66. -0.05922 0.02738 -2.163 0.0327 *
## SexMale -0.41592 0.75458 -0.551 0.5826
## AgeMonths -0.00406 0.03345 -0.121 0.9036
## Age.of.Language.Exposure..mo. 0.04673 0.02544 1.837 0.0689 .
## Hearing_Level_3_CatDeaf -1.27998 1.04954 -1.220 0.2252
## Hearing_Level_3_CatHard of Hearing -1.08567 1.02603 -1.058 0.2923
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.003 on 112 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.07064, Adjusted R-squared: 0.02085
## F-statistic: 1.419 on 6 and 112 DF, p-value: 0.2136
No model comparisons or assumption checking for best-fit Emotional Control model because we did not find one that was a good fit for the data
Step 1. Base Model:
wm_all_base <- lm(data=BRIEF_AgeOf, formula = Working.Memory_RawScore~SES..3.66.+Sex+AgeMonths)
summary(wm_all_base)
##
## Call:
## lm(formula = Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths,
## data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.558 -5.064 -1.554 3.574 23.317
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.21624 3.47771 6.676 7.88e-10 ***
## SES..3.66. -0.04614 0.04029 -1.145 0.254
## SexMale -0.37851 1.13489 -0.334 0.739
## AgeMonths 0.05260 0.04374 1.202 0.232
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.227 on 121 degrees of freedom
## Multiple R-squared: 0.02367, Adjusted R-squared: -0.0005401
## F-statistic: 0.9777 on 3 and 121 DF, p-value: 0.4057
AIC(wm_all_base)
## [1] 817.8824
Step 2. Add Age of Language Exposure:
wm_all_AoLE <- lm(data=BRIEF_AgeOf, formula = Working.Memory_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.)
summary(wm_all_AoLE)
##
## Call:
## lm(formula = Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.802 -4.034 -1.551 3.346 25.005
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.54686 3.40422 7.211 5.41e-11 ***
## SES..3.66. -0.02627 0.03968 -0.662 0.50919
## SexMale -0.74767 1.10816 -0.675 0.50117
## AgeMonths -0.00416 0.04665 -0.089 0.92910
## Age.of.Language.Exposure..mo. 0.09616 0.03284 2.928 0.00409 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.041 on 120 degrees of freedom
## Multiple R-squared: 0.08876, Adjusted R-squared: 0.05838
## F-statistic: 2.922 on 4 and 120 DF, p-value: 0.02394
AIC(wm_all_AoLE)
## [1] 811.258
Compare Model with Age of Language Exposure to base demographic model:
anova(wm_all_base, wm_all_AoLE)
## Analysis of Variance Table
##
## Model 1: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 121 4691.4
## 2 120 4378.7 1 312.78 8.5718 0.004086 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
3a. Add Age of Auditory Experience:
wm_all_AoLE_AoAE <- lm(data=BRIEF_AgeOf, formula = Working.Memory_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+AoAE)
summary(wm_all_AoLE_AoAE)
##
## Call:
## lm(formula = Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo. + AoAE, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.1890 -4.2013 -0.8549 3.4349 24.4761
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.25367 3.41120 7.110 9.32e-11 ***
## SES..3.66. -0.03116 0.03988 -0.781 0.43618
## SexMale -0.69980 1.10793 -0.632 0.52884
## AgeMonths 0.01094 0.04855 0.225 0.82211
## Age.of.Language.Exposure..mo. 0.10859 0.03467 3.132 0.00219 **
## AoAE -0.02882 0.02597 -1.110 0.26941
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.035 on 119 degrees of freedom
## Multiple R-squared: 0.09809, Adjusted R-squared: 0.06019
## F-statistic: 2.588 on 5 and 119 DF, p-value: 0.02925
AIC(wm_all_AoLE_AoAE)
## [1] 811.9713
3b. Add Length of Auditory Experience:
wm_all_AoLE_LoAE <- lm(data=BRIEF_AgeOf, formula = Working.Memory_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Length.of.Auditory.Exposure)
summary(wm_all_AoLE_LoAE)
##
## Call:
## lm(formula = Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo. + Length.of.Auditory.Exposure,
## data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.126 -4.259 -1.052 3.561 24.632
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.34208 3.41704 7.124 8.69e-11 ***
## SES..3.66. -0.02941 0.03990 -0.737 0.46243
## SexMale -0.69728 1.11112 -0.628 0.53150
## AgeMonths -0.01554 0.04863 -0.320 0.74987
## Age.of.Language.Exposure..mo. 0.10650 0.03510 3.034 0.00296 **
## Length.of.Auditory.Exposure 0.02192 0.02604 0.842 0.40161
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.048 on 119 degrees of freedom
## Multiple R-squared: 0.09415, Adjusted R-squared: 0.05609
## F-statistic: 2.474 on 5 and 119 DF, p-value: 0.03599
AIC(wm_all_AoLE_LoAE)
## [1] 812.5159
3c. Add Modality:
wm_all_AoLE_Modality <- lm(data=BRIEF_AgeOf, formula = Working.Memory_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Language_Modality)
summary(wm_all_AoLE_Modality)
##
## Call:
## lm(formula = Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo. + Language_Modality, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.037 -4.310 -1.232 3.512 24.655
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.686931 3.409935 7.240 4.81e-11 ***
## SES..3.66. -0.029494 0.039857 -0.740 0.46076
## SexMale -0.738184 1.108949 -0.666 0.50692
## AgeMonths 0.001049 0.047028 0.022 0.98224
## Age.of.Language.Exposure..mo. 0.105439 0.034391 3.066 0.00269 **
## Language_ModalityASL -1.132359 1.235983 -0.916 0.36144
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.045 on 119 degrees of freedom
## Multiple R-squared: 0.09514, Adjusted R-squared: 0.05712
## F-statistic: 2.502 on 5 and 119 DF, p-value: 0.03418
AIC(wm_all_AoLE_Modality)
## [1] 812.3794
3d. Add Hearing Status (3-category):
wm_all_AoLE_HearStat <- lm(data=BRIEF_AgeOf, formula = Working.Memory_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+ Hearing_Level_3_Cat)
summary(wm_all_AoLE_HearStat)
##
## Call:
## lm(formula = Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo. + Hearing_Level_3_Cat, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.987 -4.224 -1.291 3.498 25.012
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.458114 3.766083 6.494 2.37e-09 ***
## SES..3.66. -0.020635 0.041862 -0.493 0.6230
## SexMale -0.456516 1.153538 -0.396 0.6930
## AgeMonths -0.006845 0.051132 -0.134 0.8937
## Age.of.Language.Exposure..mo. 0.092197 0.038887 2.371 0.0195 *
## Hearing_Level_3_CatDeaf -0.145934 1.604453 -0.091 0.9277
## Hearing_Level_3_CatHard of Hearing -0.152400 1.568506 -0.097 0.9228
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.12 on 112 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.0741, Adjusted R-squared: 0.0245
## F-statistic: 1.494 on 6 and 112 DF, p-value: 0.1866
AIC(wm_all_AoLE_HearStat)
## [1] 777.6269
Compare Models from Step 3a-c to model from Step 2:
anova(wm_all_AoLE, wm_all_AoLE_AoAE)
## Analysis of Variance Table
##
## Model 1: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. +
## AoAE
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 4378.7
## 2 119 4333.8 1 44.84 1.2312 0.2694
anova(wm_all_AoLE, wm_all_AoLE_Modality)
## Analysis of Variance Table
##
## Model 1: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. +
## Language_Modality
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 4378.7
## 2 119 4348.0 1 30.668 0.8394 0.3614
anova(wm_all_AoLE, wm_all_AoLE_LoAE)
## Analysis of Variance Table
##
## Model 1: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. +
## Length.of.Auditory.Exposure
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 4378.7
## 2 119 4352.8 1 25.918 0.7086 0.4016
Checking assumptions for best-fit Working Memory model
ols_plot_resid_qq(wm_all_AoLE)
ols_test_normality(wm_all_AoLE)
## -----------------------------------------------
## Test Statistic pvalue
## -----------------------------------------------
## Shapiro-Wilk 0.9368 0.0000
## Kolmogorov-Smirnov 0.107 0.1145
## Cramer-von Mises 11.092 0.0000
## Anderson-Darling 1.9662 0.0000
## -----------------------------------------------
ols_plot_resid_hist(wm_all_AoLE)
ols_test_correlation(wm_all_AoLE)
## [1] 0.9657503
ols_plot_resid_fit(wm_all_AoLE)
#### Planning/Organization Models Step 1. Base Model:
plan_all_base <- lm(data=BRIEF_AgeOf, formula = Plan.Organize_RawScore~SES..3.66.+Sex+AgeMonths)
summary(plan_all_base)
##
## Call:
## lm(formula = Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths,
## data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9299 -2.8844 -0.5206 2.5453 9.6781
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.162934 2.120799 8.093 5.18e-13 ***
## SES..3.66. -0.039795 0.024572 -1.620 0.108
## SexMale 0.060933 0.692084 0.088 0.930
## AgeMonths -0.004365 0.026674 -0.164 0.870
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.797 on 121 degrees of freedom
## Multiple R-squared: 0.02205, Adjusted R-squared: -0.002197
## F-statistic: 0.9094 on 3 and 121 DF, p-value: 0.4387
AIC(plan_all_base)
## [1] 694.2373
Step 2. Add Age of Language Exposure:
plan_all_AoLE <- lm(data=BRIEF_AgeOf, formula = Plan.Organize_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.)
summary(plan_all_AoLE)
##
## Call:
## lm(formula = Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.705 -2.553 -0.642 2.348 9.331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.17011 2.03551 8.927 6.11e-15 ***
## SES..3.66. -0.02475 0.02372 -1.043 0.29889
## SexMale -0.21849 0.66261 -0.330 0.74217
## AgeMonths -0.04733 0.02789 -1.697 0.09237 .
## Age.of.Language.Exposure..mo. 0.07279 0.01964 3.706 0.00032 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.612 on 120 degrees of freedom
## Multiple R-squared: 0.1225, Adjusted R-squared: 0.09324
## F-statistic: 4.188 on 4 and 120 DF, p-value: 0.003286
AIC(plan_all_AoLE)
## [1] 682.6903
Compare Model with Age of Language Exposure to base demographic model:
anova(plan_all_base, plan_all_AoLE)
## Analysis of Variance Table
##
## Model 1: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 121 1744.7
## 2 120 1565.5 1 179.2 13.736 0.0003197 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
3a. Add Age of Auditory Experience:
plan_all_AoLE_AoAE <- lm(data=BRIEF_AgeOf, formula = Plan.Organize_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+AoAE)
summary(plan_all_AoLE_AoAE)
##
## Call:
## lm(formula = Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo. + AoAE, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1647 -2.4822 -0.6794 2.5480 8.9344
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.95046 2.03366 8.827 1.11e-14 ***
## SES..3.66. -0.02841 0.02378 -1.195 0.234421
## SexMale -0.18263 0.66052 -0.276 0.782650
## AgeMonths -0.03601 0.02895 -1.244 0.215880
## Age.of.Language.Exposure..mo. 0.08210 0.02067 3.972 0.000123 ***
## AoAE -0.02159 0.01548 -1.394 0.165825
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.598 on 119 degrees of freedom
## Multiple R-squared: 0.1366, Adjusted R-squared: 0.1003
## F-statistic: 3.765 on 5 and 119 DF, p-value: 0.003354
AIC(plan_all_AoLE_AoAE)
## [1] 682.6647
3b. Add Length of Auditory Experience:
plan_all_AoLE_LoAE <- lm(data=BRIEF_AgeOf, formula = Plan.Organize_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Length.of.Auditory.Exposure)
summary(plan_all_AoLE_LoAE)
##
## Call:
## lm(formula = Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo. + Length.of.Auditory.Exposure,
## data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1407 -2.5071 -0.7567 2.4447 9.0120
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.99522 2.03684 8.835 1.06e-14 ***
## SES..3.66. -0.02744 0.02378 -1.154 0.250936
## SexMale -0.17545 0.66232 -0.265 0.791541
## AgeMonths -0.05704 0.02898 -1.968 0.051388 .
## Age.of.Language.Exposure..mo. 0.08162 0.02093 3.900 0.000159 ***
## Length.of.Auditory.Exposure 0.01872 0.01552 1.206 0.230208
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.605 on 119 degrees of freedom
## Multiple R-squared: 0.1331, Adjusted R-squared: 0.09667
## F-statistic: 3.654 on 5 and 119 DF, p-value: 0.004125
AIC(plan_all_AoLE_LoAE)
## [1] 683.1718
3c. Add Modality:
plan_all_AoLE_Modality <- lm(data=BRIEF_AgeOf, formula = Plan.Organize_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+Language_Modality)
summary(plan_all_AoLE_Modality)
##
## Call:
## lm(formula = Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo. + Language_Modality, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.228 -2.581 -0.485 2.473 9.040
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.29403 2.03035 9.010 4.11e-15 ***
## SES..3.66. -0.02761 0.02373 -1.163 0.24706
## SexMale -0.21009 0.66029 -0.318 0.75090
## AgeMonths -0.04272 0.02800 -1.526 0.12978
## Age.of.Language.Exposure..mo. 0.08099 0.02048 3.955 0.00013 ***
## Language_ModalityASL -1.00189 0.73593 -1.361 0.17596
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.599 on 119 degrees of freedom
## Multiple R-squared: 0.136, Adjusted R-squared: 0.09965
## F-statistic: 3.745 on 5 and 119 DF, p-value: 0.003485
AIC(plan_all_AoLE_Modality)
## [1] 682.7585
3d. Add Hearing Status (3-category):
plan_all_AoLE_HearStat <- lm(data=BRIEF_AgeOf, formula = Plan.Organize_RawScore~SES..3.66.+Sex+AgeMonths+Age.of.Language.Exposure..mo.+ Hearing_Level_3_Cat)
summary(plan_all_AoLE_HearStat)
##
## Call:
## lm(formula = Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths +
## Age.of.Language.Exposure..mo. + Hearing_Level_3_Cat, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.179 -2.575 -0.646 2.259 9.207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.14514 2.19375 8.271 3.06e-13 ***
## SES..3.66. -0.02688 0.02438 -1.102 0.272769
## SexMale 0.10185 0.67194 0.152 0.879797
## AgeMonths -0.04375 0.02978 -1.469 0.144690
## Age.of.Language.Exposure..mo. 0.07955 0.02265 3.512 0.000642 ***
## Hearing_Level_3_CatDeaf -1.04948 0.93460 -1.123 0.263870
## Hearing_Level_3_CatHard of Hearing 0.67745 0.91366 0.741 0.459961
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.565 on 112 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.1514, Adjusted R-squared: 0.1059
## F-statistic: 3.329 on 6 and 112 DF, p-value: 0.004693
AIC(plan_all_AoLE_HearStat)
## [1] 649.0064
Compare Models from Step 3a-c to model from Step 2:
anova(plan_all_AoLE, plan_all_AoLE_AoAE)
## Analysis of Variance Table
##
## Model 1: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. +
## AoAE
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 1565.5
## 2 119 1540.3 1 25.164 1.9441 0.1658
anova(plan_all_AoLE, plan_all_AoLE_Modality)
## Analysis of Variance Table
##
## Model 1: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. +
## Language_Modality
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 1565.5
## 2 119 1541.5 1 24.008 1.8534 0.176
anova(plan_all_AoLE, plan_all_AoLE_LoAE)
## Analysis of Variance Table
##
## Model 1: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo.
## Model 2: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + Age.of.Language.Exposure..mo. +
## Length.of.Auditory.Exposure
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 1565.5
## 2 119 1546.6 1 18.903 1.4544 0.2302
Checking assumptions for best-fit Plan/Organize model
ols_plot_resid_qq(plan_all_AoLE)
ols_test_normality(plan_all_AoLE)
## -----------------------------------------------
## Test Statistic pvalue
## -----------------------------------------------
## Shapiro-Wilk 0.9725 0.0117
## Kolmogorov-Smirnov 0.0891 0.2737
## Cramer-von Mises 10.5935 0.0000
## Anderson-Darling 0.9398 0.0168
## -----------------------------------------------
ols_plot_resid_hist(plan_all_AoLE)
ols_test_correlation(plan_all_AoLE)
## [1] 0.9876708
ols_plot_resid_fit(plan_all_AoLE)
stargazer(overall_all_AoLE_AoAE, inhibition_all_AoLE_AoAE, shift_all_AoLE_AoAE, emotctrl_all_AoLE_AoAE, wm_all_AoLE_AoAE, plan_all_AoLE_AoAE, type= "html", title = "Linear Regression Results for Raw Scores", align=TRUE, dep.var.labels=c("Global Executive Composite", "Inhibition", "Shift", "Emotional Control", "Working Memory", "Planning/Organization"), covariate.labels=c("Socioeconomic Status (SES)", "Sex (Male)", "Age (Months)", "Age of Exposure to Language (Months)", "Age of Auditory Exposure (Months)"), out="Table4.html")
G <- ggplot(data=BRIEF_AgeApp, mapping = aes(x= Language_Timing, y=GEC_Tscore)) + geom_boxplot() + geom_dotplot(mapping=aes(fill=Language_Modality), dotsize = 1.1, method = "dotdensity", binaxis = "y", stackdir = "center", alpha=0.8) + theme(text = element_text(size=20), axis.title=element_text(size=20,face="bold"), legend.title=element_text(size=24), legend.text=element_text(size=24), axis.title.x=element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black")) + ylab("Global Executive Composite (T-scores)") + scale_x_discrete(labels=c("Early" = "Early Language", "Later" = "Later Language")) + scale_y_continuous(limits= c(30, 100), breaks= c(30, 40, 50, 60, 70, 80, 90, 100)) + scale_fill_grey(name = "Language Modality", start=0.1, end=0.9, ) + geom_hline(aes(yintercept= 50, linetype = "Mean"), colour= 'black') + geom_hline(aes(yintercept= 65, linetype = "Elevated"), colour= 'grey17') + scale_linetype_manual(name = "Standard Score", values=c("dotted", "longdash"), guide = guide_legend(override.aes = list(color = c("grey17", "black"))))
I <- ggplot(data=BRIEF_AgeApp, mapping = aes(x= Language_Timing, y=Inhibit_Tscore)) + geom_boxplot() + geom_dotplot(mapping=aes(fill=Language_Modality), dotsize = 1.1, method = "dotdensity", binaxis = "y", stackdir = "center", alpha=0.8) + theme(text = element_text(size=20), axis.title=element_text(size=20,face="bold"), legend.title=element_text(size=24), legend.text=element_text(size=24), axis.title.x=element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black")) + ylab("Inhibition (T-scores)") + scale_x_discrete(labels=c("Early" = "Early Language", "Later" = "Later Language")) + scale_y_continuous(limits= c(30, 100), breaks= c(30, 40, 50, 60, 70, 80, 90, 100)) + scale_fill_grey(name = "Language Modality", start=0.1, end=0.9) + geom_hline(aes(yintercept= 50, linetype = "Mean"), colour= 'black') + geom_hline(aes(yintercept= 65, linetype = "Elevated"), colour= 'grey17') + scale_linetype_manual(name = "Standard Score", values=c("dotted", "longdash"), guide = guide_legend(override.aes = list(color = c("grey17", "black"))))
S <- ggplot(data=BRIEF_AgeApp, mapping = aes(x= Language_Timing, y=Shift_Tscore)) + geom_boxplot() + geom_dotplot(mapping=aes(fill=Language_Modality), dotsize = 1.1, method = "dotdensity", binaxis = "y", stackdir = "center", alpha=0.8) + theme(text = element_text(size=20), axis.title=element_text(size=20,face="bold"), legend.title=element_text(size=24), legend.text=element_text(size=24), axis.title.x=element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black")) + ylab("Shift (T-scores)") + scale_x_discrete(labels=c("Early" = "Early Language", "Later" = "Later Language")) + scale_y_continuous(limits= c(30, 100), breaks= c(30, 40, 50, 60, 70, 80, 90, 100)) + scale_fill_grey(name = "Language Modality", start=0.1, end=0.9) + geom_hline(aes(yintercept= 50, linetype = "Mean"), colour= 'black') + geom_hline(aes(yintercept= 65, linetype = "Elevated"), colour= 'grey17') + scale_linetype_manual(name = "Standard Score", values=c("dotted", "longdash"), guide = guide_legend(override.aes = list(color = c("grey17", "black"))))
E <- ggplot(data=BRIEF_AgeApp, mapping = aes(x= Language_Timing, y= Emotional.Control_Tscore)) + geom_boxplot() + geom_dotplot(mapping=aes(fill=Language_Modality), dotsize = 1.1, method = "dotdensity", binaxis = "y", stackdir = "center", alpha=0.8) + theme(text = element_text(size=20), axis.title=element_text(size=20,face="bold"), legend.title=element_text(size=24), legend.text=element_text(size=24), axis.title.x=element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black")) + ylab("Emotional Control (T-scores)") + scale_x_discrete(labels=c("Early" = "Early Language", "Later" = "Later Language")) + scale_y_continuous(limits= c(30, 100), breaks= c(30, 40, 50, 60, 70, 80, 90, 100)) + scale_fill_grey(name = "Language Modality", start=0.1, end=0.9) + geom_hline(aes(yintercept= 50, linetype = "Mean"), colour= 'black') + geom_hline(aes(yintercept= 65, linetype = "Elevated"), colour= 'grey17') + scale_linetype_manual(name = "Standard Score", values=c("dotted", "longdash"), guide = guide_legend(override.aes = list(color = c("grey17", "black"))))
W <- ggplot(data=BRIEF_AgeApp, mapping = aes(x= Language_Timing, y= Working.Memory_Tscore)) + geom_boxplot() + geom_dotplot(mapping=aes(fill=Language_Modality), dotsize = 1.1, method = "dotdensity", binaxis = "y", stackdir = "center", alpha=0.8) + theme(text = element_text(size=20), axis.title=element_text(size=20,face="bold"), legend.title=element_text(size=24), legend.text=element_text(size=24), axis.title.x=element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black")) + ylab("Working Memory (T-scores)") + scale_x_discrete(labels=c("Early" = "Early Language", "Later" = "Later Language")) + scale_y_continuous(limits= c(30, 100), breaks= c(30, 40, 50, 60, 70, 80, 90, 100)) + scale_fill_grey(name = "Language Modality", start=0.1, end=0.9) + geom_hline(aes(yintercept= 50, linetype = "Mean"), colour= 'black') + geom_hline(aes(yintercept= 65, linetype = "Elevated"), colour= 'grey17') + scale_linetype_manual(name = "Standard Score", values=c("dotted", "longdash"), guide = guide_legend(override.aes = list(color = c("grey17", "black"))))
P <- ggplot(data=BRIEF_AgeApp, mapping = aes(x= Language_Timing, y= Plan.Organize_Tscore)) + geom_boxplot() + geom_dotplot(mapping=aes(fill=Language_Modality), dotsize = 1.1, method = "dotdensity", binaxis = "y", stackdir = "center", alpha=0.8) + theme(text = element_text(size=20), axis.title=element_text(size=20,face="bold"), legend.title=element_text(size=24), legend.text=element_text(size=24), axis.title.x=element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black")) + ylab("Plan/Organize (T-scores)") + scale_x_discrete(labels=c("Early" = "Early Language", "Later" = "Later Language")) + scale_y_continuous(limits= c(30, 100), breaks= c(30, 40, 50, 60, 70, 80, 90, 100)) + scale_fill_grey(name = "Language Modality", start=0.1, end=0.9) + geom_hline(aes(yintercept= 50, linetype = "Mean"), colour= 'black') + geom_hline(aes(yintercept= 65, linetype = "Elevated"), colour= 'grey17') + scale_linetype_manual(name = "Standard Score", values=c("dotted", "longdash"), guide = guide_legend(override.aes = list(color = c("grey17", "black"))))
ggarrange(G, I, S, E, W, P, ncol=3, nrow=2, common.legend = TRUE, legend="bottom")
BRIEF_AgeApp_GEC_over65 <- BRIEF_AgeApp %>%
group_by(LanguageGroup) %>%
summarise("Global Executive Composite T-score < 65 (n)" = sum(GEC_Tscore < 65), "Global Executive Composite T-score > 65 (n)" = sum(GEC_Tscore >= 65))
## `summarise()` ungrouping output (override with `.groups` argument)
BRIEF_AgeApp_GEC_over65 %>%
kable() %>%
kable_styling(bootstrap_options = "striped")
| LanguageGroup | Global Executive Composite T-score < 65 (n) | Global Executive Composite T-score > 65 (n) |
|---|---|---|
| Typically Hearing | 41 | 3 |
| Early ASL | 15 | 0 |
| Later English | 26 | 3 |
| Later ASL | 11 | 3 |
GEC_rr <- matrix(c(41, 3, 20, 1, 26, 3, 13, 3), 4, 2, byrow=TRUE)
dimnames(GEC_rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "GEC T-score" = c("Not Clinically Significant", "Clinically Significant"))
GEC_rr
## GEC T-score
## Group Not Clinically Significant Clinically Significant
## Typically Hearing 41 3
## Early ASL 20 1
## Later English 26 3
## Later ASL 13 3
riskratio.small(GEC_rr, verbose=TRUE)
## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect
## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect
## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect
## $x
## GEC T-score
## Group Not Clinically Significant Clinically Significant
## Typically Hearing 41 3
## Early ASL 20 1
## Later English 26 3
## Later ASL 13 3
##
## $data
## GEC T-score
## Group Not Clinically Significant Clinically Significant Total
## Typically Hearing 41 3 44
## Early ASL 20 1 21
## Later English 26 3 29
## Later ASL 13 3 16
## Total 100 10 110
##
## $p.exposed
## GEC T-score
## Group Not Clinically Significant Clinically Significant Total
## Typically Hearing 0.41 0.3 0.4000000
## Early ASL 0.20 0.1 0.1909091
## Later English 0.26 0.3 0.2636364
## Later ASL 0.13 0.3 0.1454545
## Total 1.00 1.0 1.0000000
##
## $p.outcome
## GEC T-score
## Group Not Clinically Significant Clinically Significant Total
## Typically Hearing 0.9318182 0.06818182 1
## Early ASL 0.9523810 0.04761905 1
## Later English 0.8965517 0.10344828 1
## Later ASL 0.8125000 0.18750000 1
## Total 0.9090909 0.09090909 1
##
## $measure
## risk ratio with 95% C.I.
## Group estimate lower upper
## Typically Hearing 1.0000000 NA NA
## Early ASL 0.5357143 0.05920113 4.847708
## Later English 1.1637931 0.25197780 5.375134
## Later ASL 2.1093750 0.47325381 9.401853
##
## $conf.level
## [1] 0.95
##
## $p.value
## two-sided
## Group midp.exact fisher.exact chi.square
## Typically Hearing NA NA NA
## Early ASL 0.8118073 1.0000000 0.7469897
## Later English 0.6152304 0.6762336 0.5913861
## Later ASL 0.2249223 0.3275339 0.1730802
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"
BRIEF_AgeApp_Inhibit_over65 <- BRIEF_AgeApp %>%
group_by(LanguageGroup) %>%
summarise("Inhibit T-score < 65 (n)" = sum(Inhibit_Tscore < 65), "Inhibit T-score > 65 (n)" = sum(Inhibit_Tscore >= 65))
## `summarise()` ungrouping output (override with `.groups` argument)
BRIEF_AgeApp_Inhibit_over65 %>%
kable() %>%
kable_styling(bootstrap_options = "striped")
| LanguageGroup | Inhibit T-score < 65 (n) | Inhibit T-score > 65 (n) |
|---|---|---|
| Typically Hearing | 42 | 2 |
| Early ASL | 15 | 0 |
| Later English | 27 | 2 |
| Later ASL | 13 | 1 |
Inhibit_rr <- matrix(c(42,2, 19, 2, 27, 2, 15, 1), 4, 2, byrow=TRUE)
dimnames(Inhibit_rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "GEC T-score" = c("Not Clinically Significant", "Clinically Significant"))
Inhibit_rr
## GEC T-score
## Group Not Clinically Significant Clinically Significant
## Typically Hearing 42 2
## Early ASL 19 2
## Later English 27 2
## Later ASL 15 1
riskratio.small(Inhibit_rr)
## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect
## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect
## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect
## $data
## GEC T-score
## Group Not Clinically Significant Clinically Significant Total
## Typically Hearing 42 2 44
## Early ASL 19 2 21
## Later English 27 2 29
## Later ASL 15 1 16
## Total 103 7 110
##
## $measure
## risk ratio with 95% C.I.
## Group estimate lower upper
## Typically Hearing 1.000000 NA NA
## Early ASL 1.428571 0.21586676 9.454055
## Later English 1.034483 0.15425133 6.937733
## Later ASL 0.937500 0.09109955 9.647756
##
## $p.value
## two-sided
## Group midp.exact fisher.exact chi.square
## Typically Hearing NA NA NA
## Early ASL 0.4839744 0.589206 0.4347680
## Later English 0.6919416 1.000000 0.6657999
## Later ASL 0.7836353 1.000000 0.7887764
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"
BRIEF_AgeApp_Shift_over65 <- BRIEF_AgeApp %>%
group_by(LanguageGroup) %>%
summarise("Shift T-score < 65 (n)" = sum(Shift_Tscore < 65), "Shift T-score > 65 (n)" = sum(Shift_Tscore >= 65))
## `summarise()` ungrouping output (override with `.groups` argument)
BRIEF_AgeApp_Shift_over65 %>%
kable() %>%
kable_styling(bootstrap_options = "striped")
| LanguageGroup | Shift T-score < 65 (n) | Shift T-score > 65 (n) |
|---|---|---|
| Typically Hearing | 42 | 2 |
| Early ASL | 15 | 0 |
| Later English | 26 | 3 |
| Later ASL | 13 | 1 |
Shift_rr <- matrix(c(42,2,20,1, 26, 3, 15, 1), 4, 2, byrow=TRUE)
dimnames(Shift_rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "Shift T-score" = c("Not Clinically Significant", "Clinically Significant"))
Shift_rr
## Shift T-score
## Group Not Clinically Significant Clinically Significant
## Typically Hearing 42 2
## Early ASL 20 1
## Later English 26 3
## Later ASL 15 1
riskratio.small(Shift_rr)
## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect
## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect
## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect
## $data
## Shift T-score
## Group Not Clinically Significant Clinically Significant Total
## Typically Hearing 42 2 44
## Early ASL 20 1 21
## Later English 26 3 29
## Later ASL 15 1 16
## Total 103 7 110
##
## $measure
## risk ratio with 95% C.I.
## Group estimate lower upper
## Typically Hearing 1.0000000 NA NA
## Early ASL 0.7142857 0.06856559 7.441110
## Later English 1.5517241 0.27600959 8.723783
## Later ASL 0.9375000 0.09109955 9.647756
##
## $p.value
## two-sided
## Group midp.exact fisher.exact chi.square
## Typically Hearing NA NA NA
## Early ASL 0.9387821 1.0000000 0.9689741
## Later English 0.3850968 0.3799177 0.3371029
## Later ASL 0.7836353 1.0000000 0.7887764
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"
BRIEF_AgeApp_EmoCtrl_over65 <- BRIEF_AgeApp %>%
group_by(LanguageGroup) %>%
summarise("Emotional Control T-score < 65 (n)" = sum(Emotional.Control_Tscore < 65), "Emotional Control T-score > 65 (n)" = sum(Emotional.Control_Tscore >= 65))
## `summarise()` ungrouping output (override with `.groups` argument)
BRIEF_AgeApp_EmoCtrl_over65 %>%
kable() %>%
kable_styling(bootstrap_options = "striped")
| LanguageGroup | Emotional Control T-score < 65 (n) | Emotional Control T-score > 65 (n) |
|---|---|---|
| Typically Hearing | 41 | 3 |
| Early ASL | 15 | 0 |
| Later English | 27 | 2 |
| Later ASL | 12 | 2 |
EmoCtrl_rr <- matrix(c(41,3,21,0, 27, 2, 14, 2), 4, 2, byrow=TRUE)
dimnames(EmoCtrl_rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "Emotional Control T-score" = c("Not Clinically Significant", "Clinically Significant"))
EmoCtrl_rr
## Emotional Control T-score
## Group Not Clinically Significant Clinically Significant
## Typically Hearing 41 3
## Early ASL 21 0
## Later English 27 2
## Later ASL 14 2
riskratio.small(EmoCtrl_rr)
## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect
## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect
## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect
## $data
## Emotional Control T-score
## Group Not Clinically Significant Clinically Significant Total
## Typically Hearing 41 3 44
## Early ASL 21 0 21
## Later English 27 2 29
## Later ASL 14 2 16
## Total 103 7 110
##
## $measure
## risk ratio with 95% C.I.
## Group estimate lower upper
## Typically Hearing 1.0000000 NA NA
## Early ASL 0.0000000 0.0000000 NaN
## Later English 0.7758621 0.1380048 4.361892
## Later ASL 1.4062500 0.2581260 7.661139
##
## $p.value
## two-sided
## Group midp.exact fisher.exact chi.square
## Typically Hearing NA NA NA
## Early ASL 0.3032051 0.5451923 0.2205022
## Later English 0.9732160 1.0000000 0.9896504
## Later ASL 0.5159187 0.6023050 0.4813214
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"
BRIEF_AgeApp_WorkMem_over65 <- BRIEF_AgeApp %>%
group_by(LanguageGroup) %>%
summarise("Working Memory T-score < 65 (n)" = sum(Working.Memory_Tscore < 65), "Working Memory T-score > 65 (n)" = sum(Working.Memory_Tscore >= 65))
## `summarise()` ungrouping output (override with `.groups` argument)
BRIEF_AgeApp_WorkMem_over65 %>%
kable() %>%
kable_styling(bootstrap_options = "striped")
| LanguageGroup | Working Memory T-score < 65 (n) | Working Memory T-score > 65 (n) |
|---|---|---|
| Typically Hearing | 39 | 5 |
| Early ASL | 14 | 1 |
| Later English | 25 | 4 |
| Later ASL | 12 | 2 |
WorkMem_rr <- matrix(c(39,5,18,3, 25, 4, 14, 2), 4, 2, byrow=TRUE)
dimnames(WorkMem_rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "Working Memory T-score" = c("Not Clinically Significant", "Clinically Significant"))
WorkMem_rr
## Working Memory T-score
## Group Not Clinically Significant Clinically Significant
## Typically Hearing 39 5
## Early ASL 18 3
## Later English 25 4
## Later ASL 14 2
riskratio.small(WorkMem_rr)
## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect
## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect
## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect
## $data
## Working Memory T-score
## Group Not Clinically Significant Clinically Significant Total
## Typically Hearing 39 5 44
## Early ASL 18 3 21
## Later English 25 4 29
## Later ASL 14 2 16
## Total 96 14 110
##
## $measure
## risk ratio with 95% C.I.
## Group estimate lower upper
## Typically Hearing 1.000000 NA NA
## Early ASL 1.071429 0.2823442 4.065814
## Later English 1.034483 0.3028657 3.533430
## Later ASL 0.937500 0.2016350 4.358897
##
## $p.value
## two-sided
## Group midp.exact fisher.exact chi.square
## Typically Hearing NA NA NA
## Early ASL 0.7373410 0.706303 0.7373589
## Later English 0.7619234 1.000000 0.7573605
## Later ASL 0.8792232 1.000000 0.9034907
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"
BRIEF_AgeApp_PlanOrg_over65 <- BRIEF_AgeApp %>%
group_by(LanguageGroup) %>%
summarise("Plan/Organize T-score < 65 (n)" = sum(Plan.Organize_Tscore < 65), "Plan/Organize T-score > 65 (n)" = sum(Plan.Organize_Tscore >= 65))
## `summarise()` ungrouping output (override with `.groups` argument)
BRIEF_AgeApp_PlanOrg_over65 %>%
kable() %>%
kable_styling(bootstrap_options = "striped")
| LanguageGroup | Plan/Organize T-score < 65 (n) | Plan/Organize T-score > 65 (n) |
|---|---|---|
| Typically Hearing | 41 | 3 |
| Early ASL | 15 | 0 |
| Later English | 25 | 4 |
| Later ASL | 13 | 1 |
PlanOrg_rr <- matrix(c(41,3,20,1, 25, 4, 15, 1), 4, 2, byrow=TRUE)
dimnames(PlanOrg_rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "Plan/Organize T-score" = c("Not Clinically Significant", "Clinically Significant"))
PlanOrg_rr
## Plan/Organize T-score
## Group Not Clinically Significant Clinically Significant
## Typically Hearing 41 3
## Early ASL 20 1
## Later English 25 4
## Later ASL 15 1
riskratio.small(PlanOrg_rr)
## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect
## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect
## Warning in chisq.test(xx, correct = correction): Chi-squared approximation may
## be incorrect
## $data
## Plan/Organize T-score
## Group Not Clinically Significant Clinically Significant Total
## Typically Hearing 41 3 44
## Early ASL 20 1 21
## Later English 25 4 29
## Later ASL 15 1 16
## Total 101 9 110
##
## $measure
## risk ratio with 95% C.I.
## Group estimate lower upper
## Typically Hearing 1.0000000 NA NA
## Early ASL 0.5357143 0.05920113 4.847708
## Later English 1.5517241 0.37445613 6.430253
## Later ASL 0.7031250 0.07871935 6.280346
##
## $p.value
## two-sided
## Group midp.exact fisher.exact chi.square
## Typically Hearing NA NA NA
## Early ASL 0.8118073 1.0000000 0.7469897
## Later English 0.3585273 0.4249522 0.3219848
## Later ASL 0.9913275 1.0000000 0.9378094
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"
Possible Issue: Because Age of Language Exposure and Age of Auditory Exposure were significantly correlated, we need to determine whether they might actually be measuring same underlying construct
Approach: See whether findings above (where Language but not Auditory exposure significantly predicts BRIEF scores) hold when predictors are added to the model in a different order
overall_all_base <- lm(data=BRIEF_AgeOf, formula = GEC_RawScore~SES..3.66.+Sex+AgeMonths)
summary(overall_all_base)
##
## Call:
## lm(formula = GEC_RawScore ~ SES..3.66. + Sex + AgeMonths, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -49.301 -16.784 -0.489 10.601 60.607
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 96.5244 11.0764 8.714 1.83e-14 ***
## SES..3.66. -0.3029 0.1283 -2.360 0.0199 *
## SexMale -0.6580 3.6146 -0.182 0.8559
## AgeMonths 0.1772 0.1393 1.272 0.2057
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.83 on 121 degrees of freedom
## Multiple R-squared: 0.05657, Adjusted R-squared: 0.03318
## F-statistic: 2.419 on 3 and 121 DF, p-value: 0.06954
overall_all_AoAE <- lm(data=BRIEF_AgeOf, formula = GEC_RawScore~SES..3.66.+Sex+AgeMonths+AoAE)
summary(overall_all_AoAE)
##
## Call:
## lm(formula = GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE,
## data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -49.168 -16.628 -0.379 10.425 61.047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 96.79798 11.19724 8.645 2.81e-14 ***
## SES..3.66. -0.29849 0.13054 -2.286 0.024 *
## SexMale -0.71423 3.63891 -0.196 0.845
## AgeMonths 0.16404 0.15345 1.069 0.287
## AoAE 0.01695 0.08109 0.209 0.835
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.91 on 120 degrees of freedom
## Multiple R-squared: 0.05692, Adjusted R-squared: 0.02548
## F-statistic: 1.81 on 4 and 120 DF, p-value: 0.1312
anova(overall_all_base, overall_all_AoAE)
## Analysis of Variance Table
##
## Model 1: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 121 47591
## 2 120 47573 1 17.32 0.0437 0.8348
overall_all_AoAE_AoLE <- lm(data=BRIEF_AgeOf, formula = GEC_RawScore~SES..3.66.+Sex+AgeMonths+AoAE+Age.of.Language.Exposure..mo.)
summary(overall_all_AoAE_AoLE)
##
## Call:
## lm(formula = GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE +
## Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -54.475 -13.271 0.398 10.333 65.083
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 100.35055 10.83174 9.264 1.03e-15 ***
## SES..3.66. -0.24696 0.12664 -1.950 0.05351 .
## SexMale -1.80034 3.51807 -0.512 0.60978
## AgeMonths 0.02029 0.15417 0.132 0.89550
## AoAE -0.06959 0.08247 -0.844 0.40045
## Age.of.Language.Exposure..mo. 0.35769 0.11010 3.249 0.00151 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.16 on 119 degrees of freedom
## Multiple R-squared: 0.1338, Adjusted R-squared: 0.09735
## F-statistic: 3.675 on 5 and 119 DF, p-value: 0.003968
anova(overall_all_AoAE, overall_all_AoAE_AoLE)
## Analysis of Variance Table
##
## Model 1: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
## Model 2: GEC_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE + Age.of.Language.Exposure..mo.
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 47573
## 2 119 43697 1 3875.9 10.555 0.001506 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#~~~~~#
inhibition_all_base <- lm(data=BRIEF_AgeOf, formula = Inhibit_RawScore~SES..3.66.+Sex+AgeMonths)
summary(inhibition_all_base)
##
## Call:
## lm(formula = Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths,
## data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.3759 -4.6435 -0.0437 4.4091 21.5108
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.35171 3.52670 6.338 4.16e-09 ***
## SES..3.66. -0.08194 0.04086 -2.005 0.0472 *
## SexMale 0.82837 1.15087 0.720 0.4730
## AgeMonths 0.08672 0.04436 1.955 0.0529 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.314 on 121 degrees of freedom
## Multiple R-squared: 0.06463, Adjusted R-squared: 0.04144
## F-statistic: 2.787 on 3 and 121 DF, p-value: 0.04365
inhibition_all_AoAE <- lm(data=BRIEF_AgeOf, formula = Inhibit_RawScore~SES..3.66.+Sex+AgeMonths+AoAE)
summary(inhibition_all_AoAE)
##
## Call:
## lm(formula = Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths +
## AoAE, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.3479 -4.6420 -0.1239 4.4228 21.6034
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.409374 3.565524 6.285 5.49e-09 ***
## SES..3.66. -0.081015 0.041569 -1.949 0.0536 .
## SexMale 0.816525 1.158735 0.705 0.4824
## AgeMonths 0.083942 0.048864 1.718 0.0884 .
## AoAE 0.003573 0.025822 0.138 0.8902
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.34 on 120 degrees of freedom
## Multiple R-squared: 0.06478, Adjusted R-squared: 0.0336
## F-statistic: 2.078 on 4 and 120 DF, p-value: 0.08789
anova(inhibition_all_base, inhibition_all_AoAE)
## Analysis of Variance Table
##
## Model 1: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 121 4824.5
## 2 120 4823.8 1 0.76955 0.0191 0.8902
inhibition_all_AoAE_AoLE <- lm(data=BRIEF_AgeOf, formula = Inhibit_RawScore~SES..3.66.+Sex+AgeMonths+AoAE+Age.of.Language.Exposure..mo.)
summary(inhibition_all_AoAE_AoLE)
##
## Call:
## lm(formula = Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths +
## AoAE + Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.4387 -4.1619 -0.5115 4.2057 22.4330
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.13959 3.53725 6.542 1.6e-09 ***
## SES..3.66. -0.07042 0.04135 -1.703 0.0912 .
## SexMale 0.59328 1.14887 0.516 0.6065
## AgeMonths 0.05440 0.05035 1.080 0.2821
## AoAE -0.01421 0.02693 -0.528 0.5986
## Age.of.Language.Exposure..mo. 0.07352 0.03595 2.045 0.0431 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.258 on 119 degrees of freedom
## Multiple R-squared: 0.09652, Adjusted R-squared: 0.05856
## F-statistic: 2.543 on 5 and 119 DF, p-value: 0.03178
anova(inhibition_all_AoAE, inhibition_all_AoAE_AoLE)
## Analysis of Variance Table
##
## Model 1: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
## Model 2: Inhibit_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE + Age.of.Language.Exposure..mo.
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 4823.8
## 2 119 4660.0 1 163.75 4.1817 0.04307 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#~~~~~#
shift_all_base <- lm(data=BRIEF_AgeOf, formula = Shift_RawScore~SES..3.66.+Sex+AgeMonths)
summary(shift_all_base)
##
## Call:
## lm(formula = Shift_RawScore ~ SES..3.66. + Sex + AgeMonths, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.4632 -2.8668 -0.6954 2.0394 15.2974
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.752518 2.046755 8.185 3.17e-13 ***
## SES..3.66. -0.060132 0.023714 -2.536 0.0125 *
## SexMale -0.234463 0.667921 -0.351 0.7262
## AgeMonths 0.008393 0.025743 0.326 0.7450
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.665 on 121 degrees of freedom
## Multiple R-squared: 0.05149, Adjusted R-squared: 0.02797
## F-statistic: 2.189 on 3 and 121 DF, p-value: 0.09279
shift_all_AoAE <- lm(data=BRIEF_AgeOf, formula = Shift_RawScore~SES..3.66.+Sex+AgeMonths+AoAE)
summary(shift_all_AoAE)
##
## Call:
## lm(formula = Shift_RawScore ~ SES..3.66. + Sex + AgeMonths +
## AoAE, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9645 -2.8688 -0.6796 2.0712 15.0365
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.971207 2.062395 8.229 2.62e-13 ***
## SES..3.66. -0.056624 0.024045 -2.355 0.0201 *
## SexMale -0.279398 0.670244 -0.417 0.6775
## AgeMonths -0.002155 0.028264 -0.076 0.9393
## AoAE 0.013549 0.014936 0.907 0.3662
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.667 on 120 degrees of freedom
## Multiple R-squared: 0.05795, Adjusted R-squared: 0.02654
## F-statistic: 1.845 on 4 and 120 DF, p-value: 0.1246
anova(shift_all_base, shift_all_AoAE)
## Analysis of Variance Table
##
## Model 1: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 121 1625.0
## 2 120 1613.9 1 11.066 0.8228 0.3662
shift_all_AoAE_AoLE <- lm(data=BRIEF_AgeOf, formula = Shift_RawScore~SES..3.66.+Sex+AgeMonths+AoAE+Age.of.Language.Exposure..mo.)
summary(shift_all_AoAE_AoLE)
##
## Call:
## lm(formula = Shift_RawScore ~ SES..3.66. + Sex + AgeMonths +
## AoAE + Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.8804 -2.3444 -0.3614 2.1039 14.0925
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.672586 1.981853 8.917 6.81e-15 ***
## SES..3.66. -0.046451 0.023170 -2.005 0.047258 *
## SexMale -0.493827 0.643691 -0.767 0.444494
## AgeMonths -0.030535 0.028208 -1.082 0.281235
## AoAE -0.003536 0.015088 -0.234 0.815094
## Age.of.Language.Exposure..mo. 0.070618 0.020144 3.506 0.000643 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.506 on 119 degrees of freedom
## Multiple R-squared: 0.1461, Adjusted R-squared: 0.1103
## F-statistic: 4.073 on 5 and 119 DF, p-value: 0.001896
anova(shift_all_AoAE, shift_all_AoAE_AoLE)
## Analysis of Variance Table
##
## Model 1: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
## Model 2: Shift_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE + Age.of.Language.Exposure..mo.
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 1613.9
## 2 119 1462.8 1 151.08 12.29 0.000643 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#~~~~~#
emotctrl_all_base <- lm(data=BRIEF_AgeOf, formula = Emotional.Control_RawScore~SES..3.66.+Sex+AgeMonths)
emotctrl_all_AoAE <- lm(data=BRIEF_AgeOf, formula = Emotional.Control_RawScore~SES..3.66.+Sex+AgeMonths+AoAE)
summary(emotctrl_all_AoAE)
##
## Call:
## lm(formula = Emotional.Control_RawScore ~ SES..3.66. + Sex +
## AgeMonths + AoAE, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1562 -3.0647 -0.6891 2.3727 14.1389
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.4271894 2.2670480 7.687 4.59e-12 ***
## SES..3.66. -0.0622427 0.0264307 -2.355 0.0201 *
## SexMale -0.3906305 0.7367523 -0.530 0.5969
## AgeMonths 0.0166440 0.0310691 0.536 0.5931
## AoAE -0.0001941 0.0164184 -0.012 0.9906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.031 on 120 degrees of freedom
## Multiple R-squared: 0.049, Adjusted R-squared: 0.0173
## F-statistic: 1.546 on 4 and 120 DF, p-value: 0.1933
anova(emotctrl_all_base, emotctrl_all_AoAE)
## Analysis of Variance Table
##
## Model 1: Emotional.Control_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Emotional.Control_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 121 1950.1
## 2 120 1950.1 1 0.0022716 1e-04 0.9906
emotctrl_all_AoAE_AoLE <- lm(data=BRIEF_AgeOf, formula = Emotional.Control_RawScore~SES..3.66.+Sex+AgeMonths+AoAE+Age.of.Language.Exposure..mo.)
summary(emotctrl_all_AoAE_AoLE)
##
## Call:
## lm(formula = Emotional.Control_RawScore ~ SES..3.66. + Sex +
## AgeMonths + AoAE + Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1383 -2.8960 -0.6379 2.6331 13.6172
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.8147979 2.2610112 7.879 1.74e-12 ***
## SES..3.66. -0.0566203 0.0264338 -2.142 0.0342 *
## SexMale -0.5091322 0.7343598 -0.693 0.4895
## AgeMonths 0.0009605 0.0321817 0.030 0.9762
## AoAE -0.0096359 0.0172137 -0.560 0.5767
## Age.of.Language.Exposure..mo. 0.0390263 0.0229814 1.698 0.0921 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4 on 119 degrees of freedom
## Multiple R-squared: 0.0715, Adjusted R-squared: 0.03249
## F-statistic: 1.833 on 5 and 119 DF, p-value: 0.1115
anova(emotctrl_all_AoAE, emotctrl_all_AoAE_AoLE)
## Analysis of Variance Table
##
## Model 1: Emotional.Control_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
## Model 2: Emotional.Control_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE +
## Age.of.Language.Exposure..mo.
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 1950.1
## 2 119 1904.0 1 46.14 2.8838 0.09209 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#~~~~~#
wm_all_base <- lm(data=BRIEF_AgeOf, formula = Working.Memory_RawScore~SES..3.66.+Sex+AgeMonths)
summary(wm_all_base)
##
## Call:
## lm(formula = Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths,
## data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.558 -5.064 -1.554 3.574 23.317
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.21624 3.47771 6.676 7.88e-10 ***
## SES..3.66. -0.04614 0.04029 -1.145 0.254
## SexMale -0.37851 1.13489 -0.334 0.739
## AgeMonths 0.05260 0.04374 1.202 0.232
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.227 on 121 degrees of freedom
## Multiple R-squared: 0.02367, Adjusted R-squared: -0.0005401
## F-statistic: 0.9777 on 3 and 121 DF, p-value: 0.4057
wm_all_AoAE <- lm(data=BRIEF_AgeOf, formula = Working.Memory_RawScore~SES..3.66.+Sex+AgeMonths+AoAE)
summary(wm_all_AoAE)
##
## Call:
## lm(formula = Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths +
## AoAE, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.578 -5.055 -1.611 3.673 23.251
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.175157 3.516130 6.591 1.23e-09 ***
## SES..3.66. -0.046803 0.040993 -1.142 0.256
## SexMale -0.370072 1.142683 -0.324 0.747
## AgeMonths 0.054579 0.048187 1.133 0.260
## AoAE -0.002545 0.025465 -0.100 0.921
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.252 on 120 degrees of freedom
## Multiple R-squared: 0.02375, Adjusted R-squared: -0.008794
## F-statistic: 0.7298 on 4 and 120 DF, p-value: 0.5733
anova(wm_all_base, wm_all_AoAE)
## Analysis of Variance Table
##
## Model 1: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 121 4691.4
## 2 120 4691.1 1 0.3906 0.01 0.9205
wm_all_AoAE_AoLE <- lm(data=BRIEF_AgeOf, formula = Working.Memory_RawScore~SES..3.66.+Sex+AgeMonths+AoAE+Age.of.Language.Exposure..mo.)
summary(wm_all_AoAE_AoLE)
##
## Call:
## lm(formula = Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths +
## AoAE + Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.1890 -4.2013 -0.8549 3.4349 24.4761
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.25367 3.41120 7.110 9.32e-11 ***
## SES..3.66. -0.03116 0.03988 -0.781 0.43618
## SexMale -0.69980 1.10793 -0.632 0.52884
## AgeMonths 0.01094 0.04855 0.225 0.82211
## AoAE -0.02882 0.02597 -1.110 0.26941
## Age.of.Language.Exposure..mo. 0.10859 0.03467 3.132 0.00219 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.035 on 119 degrees of freedom
## Multiple R-squared: 0.09809, Adjusted R-squared: 0.06019
## F-statistic: 2.588 on 5 and 119 DF, p-value: 0.02925
anova(wm_all_AoAE, wm_all_AoAE_AoLE)
## Analysis of Variance Table
##
## Model 1: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
## Model 2: Working.Memory_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE +
## Age.of.Language.Exposure..mo.
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 4691.1
## 2 119 4333.8 1 357.23 9.8088 0.002186 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#~~~~~#
plan_all_base <- lm(data=BRIEF_AgeOf, formula = Plan.Organize_RawScore~SES..3.66.+Sex+AgeMonths)
summary(plan_all_base)
##
## Call:
## lm(formula = Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths,
## data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9299 -2.8844 -0.5206 2.5453 9.6781
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.162934 2.120799 8.093 5.18e-13 ***
## SES..3.66. -0.039795 0.024572 -1.620 0.108
## SexMale 0.060933 0.692084 0.088 0.930
## AgeMonths -0.004365 0.026674 -0.164 0.870
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.797 on 121 degrees of freedom
## Multiple R-squared: 0.02205, Adjusted R-squared: -0.002197
## F-statistic: 0.9094 on 3 and 121 DF, p-value: 0.4387
plan_all_AoAE <- lm(data=BRIEF_AgeOf, formula = Plan.Organize_RawScore~SES..3.66.+Sex+AgeMonths+AoAE)
summary(plan_all_AoAE)
##
## Call:
## lm(formula = Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths +
## AoAE, data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9602 -2.9064 -0.4391 2.5251 9.6625
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.135079 2.144209 7.991 9.25e-13 ***
## SES..3.66. -0.040242 0.024999 -1.610 0.110
## SexMale 0.066657 0.696832 0.096 0.924
## AgeMonths -0.003021 0.029386 -0.103 0.918
## AoAE -0.001726 0.015529 -0.111 0.912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.813 on 120 degrees of freedom
## Multiple R-squared: 0.02215, Adjusted R-squared: -0.01044
## F-statistic: 0.6796 on 4 and 120 DF, p-value: 0.6074
anova(plan_all_base, plan_all_AoAE)
## Analysis of Variance Table
##
## Model 1: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths
## Model 2: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 121 1744.7
## 2 120 1744.5 1 0.17953 0.0123 0.9117
plan_all_AoAE_AoLE <- lm(data=BRIEF_AgeOf, formula = Plan.Organize_RawScore~SES..3.66.+Sex+AgeMonths+AoAE+Age.of.Language.Exposure..mo.)
summary(plan_all_AoAE_AoLE)
##
## Call:
## lm(formula = Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths +
## AoAE + Age.of.Language.Exposure..mo., data = BRIEF_AgeOf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1647 -2.4822 -0.6794 2.5480 8.9344
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.95046 2.03366 8.827 1.11e-14 ***
## SES..3.66. -0.02841 0.02378 -1.195 0.234421
## SexMale -0.18263 0.66052 -0.276 0.782650
## AgeMonths -0.03601 0.02895 -1.244 0.215880
## AoAE -0.02159 0.01548 -1.394 0.165825
## Age.of.Language.Exposure..mo. 0.08210 0.02067 3.972 0.000123 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.598 on 119 degrees of freedom
## Multiple R-squared: 0.1366, Adjusted R-squared: 0.1003
## F-statistic: 3.765 on 5 and 119 DF, p-value: 0.003354
anova(plan_all_AoAE, plan_all_AoAE_AoLE)
## Analysis of Variance Table
##
## Model 1: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE
## Model 2: Plan.Organize_RawScore ~ SES..3.66. + Sex + AgeMonths + AoAE +
## Age.of.Language.Exposure..mo.
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 120 1744.5
## 2 119 1540.3 1 204.18 15.774 0.0001227 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Findings: For all subscales, adding Age of Auditory Exposure to the model with demographic variables did not improve model fit (and Age of Auditory Exposure was not a significant predictor of any subsale), but subsequently adding Age of Language exposure to the model with Age of Auditory Exposure and demographic variables did significantly improve model fit (and Age of Language Exposure was a significant predictor of BRIEF-P scores in almost all models).
Possible issue: If this “non-age-appropriate” group of participants is largely from the Early ASL group, and/or has higher SES, our finding that Early exposure to language is the driving factor in EF development may not be valid
Approach: Checking the Age, SES, and Language Group of participants who were “age-appropriate” for the BRIEF-P compared to those who were not
table1::label(BRIEF_AgeOf$AgeMonths) <- "Age (Months)"
table1::label(BRIEF_AgeOf$SES..3.66.) <- "SES"
table1::label(BRIEF_AgeOf$LanguageGroup) <- "Language Group"
table1::table1(~AgeMonths + SES..3.66. + LanguageGroup | Age.Appropriate., data = BRIEF_AgeOf, overall=T)
| no (N=23) |
yes (N=102) |
TRUE (N=125) |
|
|---|---|---|---|
| Age (Months) | |||
| Mean (SD) | 77.8 (5.70) | 54.4 (9.80) | 58.7 (12.9) |
| Median [Min, Max] | 76.0 [72.0, 91.0] | 55.0 [37.0, 71.0] | 58.0 [37.0, 91.0] |
| SES | |||
| Mean (SD) | 47.0 (20.0) | 51.7 (12.2) | 50.8 (14.0) |
| Median [Min, Max] | 58.5 [8.00, 66.0] | 54.3 [3.00, 66.0] | 54.5 [3.00, 66.0] |
| Language Group | |||
| Typically Hearing | 2 (8.7%) | 44 (43.1%) | 46 (36.8%) |
| Early ASL | 5 (21.7%) | 15 (14.7%) | 20 (16.0%) |
| Later English | 6 (26.1%) | 29 (28.4%) | 35 (28.0%) |
| Later ASL | 10 (43.5%) | 14 (13.7%) | 24 (19.2%) |
Participants in the Later groups, specifically the Later ASL group, are over-represented in not having received age-appropriate BRIEF. This should not be a concern for our findings, because if the behaviors surveyed in the BRIEF-P were not appropriate for older kids, then we would not expect the older Later ASL group to have elevated BRIEF scores relative to early-exposed kids–but they do.
Approach Pt 2: Re-run Chi-square with only age-appropriate subset to confirm the non-difference isn’t driven by older Early ASL participants Create dataframe with only age-appropriate participants exposed to language early
BRIEF_early_AgeApp <- subset(BRIEF_AgeApp, BRIEF_AgeApp$Language_Timing=="Early")
BRIEF Scores & Welch two sample t-tests for two “Early” groups within Age-appropriate subset
table1::label(BRIEF_early_AgeApp$GEC_RawScore) <- "Global Executive Composite"
table1::label(BRIEF_early_AgeApp$Inhibit_RawScore) <- "Inhibition"
table1::label(BRIEF_early_AgeApp$Shift_RawScore) <- "Shift"
table1::label(BRIEF_early_AgeApp$Emotional.Control_RawScore) <- "Emotional Control"
table1::label(BRIEF_early_AgeApp$Working.Memory_RawScore) <- "Working Memory"
table1::label(BRIEF_early_AgeApp$Plan.Organize_RawScore) <- "Plan/Organize"
table1(~GEC_RawScore + Inhibit_RawScore + Shift_RawScore + Emotional.Control_RawScore + Working.Memory_RawScore + Plan.Organize_RawScore | Language_Modality, data = BRIEF_early_AgeApp, overall=F)
| English (N=44) |
ASL (N=15) |
|
|---|---|---|
| Global Executive Composite | ||
| Mean (SD) | 88.4 (18.9) | 85.8 (13.5) |
| Median [Min, Max] | 88.0 [63.0, 155] | 88.0 [63.0, 103] |
| Inhibition | ||
| Mean (SD) | 22.8 (6.00) | 22.9 (4.90) |
| Median [Min, Max] | 23.0 [16.0, 46.0] | 22.0 [16.0, 30.0] |
| Shift | ||
| Mean (SD) | 13.2 (3.11) | 13.5 (2.67) |
| Median [Min, Max] | 12.0 [10.0, 24.0] | 13.0 [10.0, 17.0] |
| Emotional Control | ||
| Mean (SD) | 15.1 (3.69) | 14.7 (2.96) |
| Median [Min, Max] | 14.5 [10.0, 24.0] | 14.0 [10.0, 19.0] |
| Working Memory | ||
| Mean (SD) | 22.9 (6.72) | 21.6 (4.27) |
| Median [Min, Max] | 21.0 [17.0, 48.0] | 21.0 [17.0, 31.0] |
| Plan/Organize | ||
| Mean (SD) | 14.4 (3.44) | 13.1 (2.42) |
| Median [Min, Max] | 14.0 [10.0, 23.0] | 13.0 [10.0, 17.0] |
t.test(BRIEF_early_AgeApp$GEC_RawScore~BRIEF_early_AgeApp$Language_Modality)
##
## Welch Two Sample t-test
##
## data: BRIEF_early_AgeApp$GEC_RawScore by BRIEF_early_AgeApp$Language_Modality
## t = 0.57581, df = 34.041, p-value = 0.5685
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -6.541513 11.714240
## sample estimates:
## mean in group English mean in group ASL
## 88.38636 85.80000
t.test(BRIEF_early_AgeApp$Inhibit_RawScore~BRIEF_early_AgeApp$Language_Modality)
##
## Welch Two Sample t-test
##
## data: BRIEF_early_AgeApp$Inhibit_RawScore by BRIEF_early_AgeApp$Language_Modality
## t = -0.016572, df = 29.461, p-value = 0.9869
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.202462 3.150947
## sample estimates:
## mean in group English mean in group ASL
## 22.84091 22.86667
t.test(BRIEF_early_AgeApp$Shift_RawScore~BRIEF_early_AgeApp$Language_Modality)
##
## Welch Two Sample t-test
##
## data: BRIEF_early_AgeApp$Shift_RawScore by BRIEF_early_AgeApp$Language_Modality
## t = -0.36901, df = 28.005, p-value = 0.7149
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.014922 1.399771
## sample estimates:
## mean in group English mean in group ASL
## 13.15909 13.46667
t.test(BRIEF_early_AgeApp$Emotional.Control_RawScore~BRIEF_early_AgeApp$Language_Modality)
##
## Welch Two Sample t-test
##
## data: BRIEF_early_AgeApp$Emotional.Control_RawScore by BRIEF_early_AgeApp$Language_Modality
## t = 0.40191, df = 30.011, p-value = 0.6906
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.552126 2.312732
## sample estimates:
## mean in group English mean in group ASL
## 15.11364 14.73333
t.test(BRIEF_early_AgeApp$Working.Memory_RawScore~BRIEF_early_AgeApp$Language_Modality)
##
## Welch Two Sample t-test
##
## data: BRIEF_early_AgeApp$Working.Memory_RawScore by BRIEF_early_AgeApp$Language_Modality
## t = 0.84371, df = 38.617, p-value = 0.404
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.766731 4.294004
## sample estimates:
## mean in group English mean in group ASL
## 22.86364 21.60000
t.test(BRIEF_early_AgeApp$Plan.Organize_RawScore~BRIEF_early_AgeApp$Language_Modality)
##
## Welch Two Sample t-test
##
## data: BRIEF_early_AgeApp$Plan.Organize_RawScore by BRIEF_early_AgeApp$Language_Modality
## t = 1.5727, df = 34.639, p-value = 0.1249
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3716748 2.9231899
## sample estimates:
## mean in group English mean in group ASL
## 14.40909 13.13333
stargazer(overall_all_base, overall_all_AoLE, overall_all_AoLE_AoAE, overall_all_AoLE_LoAE, overall_all_AoLE_Modality,overall_all_AoLE_HearStat, type= "html", title = "Linear Regression Results", align=TRUE, dep.var.labels=c("Global Executive Composite Raw Scores"), covariate.labels=c("Socioeconomic Status (SES)", "Sex (Male)", "Age (Months)", "Age of Exposure to Language (Months)", "Age of Auditory Exposure (Months)", "Length of Auditory Experience (Months)", "Language Modality (ASL)", "Hearing Status (Deaf)", "Hearing Status (HoH)"), out="BRIEF_supp_table1.html")
stargazer(inhibition_all_base, inhibition_all_AoLE, inhibition_all_AoLE_AoAE, inhibition_all_AoLE_LoAE, inhibition_all_AoLE_Modality, inhibition_all_AoLE_HearStat, type= "html", title = "Linear Regression Results", align=TRUE, dep.var.labels=c("Inhibition Raw Scores"), covariate.labels=c("Socioeconomic Status (SES)", "Sex (Male)", "Age (Months)", "Age of Exposure to Language (Months)", "Age of Auditory Exposure (Months)", "Length of Auditory Experience (Months)", "Language Modality (ASL)", "Hearing Status (Deaf)", "Hearing Status (HoH)"), out="BRIEF_supp_table2.html")
stargazer(shift_all_base, shift_all_AoLE, shift_all_AoLE_AoAE, shift_all_AoLE_LoAE, shift_all_AoLE_Modality, shift_all_AoLE_HearStat, type= "html", title = "Linear Regression Results", align=TRUE, dep.var.labels=c("Shift Raw Scores"), covariate.labels=c("Socioeconomic Status (SES)", "Sex (Male)", "Age (Months)", "Age of Exposure to Language (Months)", "Age of Auditory Exposure (Months)", "Length of Auditory Experience (Months)", "Language Modality (ASL)", "Hearing Status (Deaf)", "Hearing Status (HoH)"), out="BRIEF_supp_table3.html")
stargazer(emotctrl_all_base, emotctrl_all_AoLE, emotctrl_all_AoLE_AoAE, emotctrl_all_AoLE_LoAE, emotctrl_all_AoLE_Modality, emotctrl_all_AoLE_HearStat, type= "html", title = "Linear Regression Results", align=TRUE, dep.var.labels=c("Emotional Control Raw Scores"), covariate.labels=c("Socioeconomic Status (SES)", "Sex (Male)", "Age (Months)", "Age of Exposure to Language (Months)", "Age of Auditory Exposure (Months)", "Length of Auditory Experience (Months)", "Language Modality (ASL)", "Hearing Status (Deaf)", "Hearing Status (HoH)"), out="BRIEF_supp_table4.html")
stargazer(wm_all_base, wm_all_AoLE, wm_all_AoLE_AoAE, wm_all_AoLE_LoAE, wm_all_AoLE_Modality,wm_all_AoLE_HearStat, type= "html", title = "Linear Regression Results", align=TRUE, dep.var.labels=c("Working Memory Raw Scores"), covariate.labels=c("Socioeconomic Status (SES)", "Sex (Male)", "Age (Months)", "Age of Exposure to Language (Months)", "Age of Auditory Exposure (Months)", "Length of Auditory Experience (Months)", "Language Modality (ASL)", "Hearing Status (Deaf)", "Hearing Status (HoH)"), out="BRIEF_supp_table5.html")
stargazer(plan_all_base, plan_all_AoLE, plan_all_AoLE_AoAE, plan_all_AoLE_LoAE, plan_all_AoLE_Modality, plan_all_AoLE_HearStat, type= "html", title = "Linear Regression Results", align=TRUE, dep.var.labels=c("Planning/Organization Raw Scores"), covariate.labels=c("Socioeconomic Status (SES)", "Sex (Male)", "Age (Months)", "Age of Exposure to Language (Months)", "Age of Auditory Exposure (Months)", "Length of Auditory Experience (Months)", "Language Modality (ASL)", "Hearing Status (Deaf)", "Hearing Status (HoH)"), out="BRIEF_supp_table6.html")
table1::label(BRIEF_AgeApp$AgeMonths) <- "Age (Months)"
table1::label(BRIEF_AgeApp$Sex) <- "Sex"
table1::label(BRIEF_AgeApp$Race_recoded) <- "Race"
table1::label(BRIEF_AgeApp$Ethnicity_recoded) <- "Ethnicity"
table1::label(BRIEF_AgeApp$SES..3.66.) <- "SES"
table1::table1(~AgeMonths + Sex + Race_recoded + Ethnicity_recoded + SES..3.66. | LanguageGroup, data = BRIEF_AgeApp, overall=F)
| Typically Hearing (N=44) |
Early ASL (N=15) |
Later English (N=29) |
Later ASL (N=14) |
|
|---|---|---|---|---|
| Age (Months) | ||||
| Mean (SD) | 53.7 (9.80) | 53.3 (8.70) | 55.2 (10.0) | 56.3 (11.0) |
| Median [Min, Max] | 54.0 [37.0, 71.0] | 54.0 [41.0, 70.0] | 58.0 [37.0, 71.0] | 57.0 [37.0, 71.0] |
| Sex | ||||
| Female | 23 (52.3%) | 7 (46.7%) | 15 (51.7%) | 7 (50.0%) |
| Male | 21 (47.7%) | 8 (53.3%) | 14 (48.3%) | 7 (50.0%) |
| Race | ||||
| Asian | 0 (0%) | 0 (0%) | 1 (3.4%) | 2 (14.3%) |
| Black or African American | 0 (0%) | 0 (0%) | 1 (3.4%) | 0 (0%) |
| White | 41 (93.2%) | 14 (93.3%) | 22 (75.9%) | 11 (78.6%) |
| More than one | 3 (6.8%) | 0 (0%) | 3 (10.3%) | 1 (7.1%) |
| Other/Missing | 0 (0%) | 1 (6.7%) | 2 (6.9%) | 0 (0%) |
| Ethnicity | ||||
| Hispanic | 3 (6.8%) | 0 (0%) | 3 (10.3%) | 1 (7.1%) |
| Non-Hispanic | 40 (90.9%) | 9 (60.0%) | 22 (75.9%) | 11 (78.6%) |
| Prefer not to answer | 0 (0%) | 1 (6.7%) | 0 (0%) | 0 (0%) |
| Missing | 1 (2.3%) | 5 (33.3%) | 4 (13.8%) | 2 (14.3%) |
| SES | ||||
| Mean (SD) | 55.5 (8.83) | 46.3 (16.5) | 49.2 (13.7) | 50.6 (9.57) |
| Median [Min, Max] | 56.0 [21.5, 66.0] | 55.0 [19.0, 66.0] | 53.0 [3.00, 66.0] | 53.0 [25.5, 62.0] |
BRIEF_AgeApp_GEC_over60 <- BRIEF_AgeApp %>%
group_by(LanguageGroup) %>%
summarise("Global Executive Composite T-score < 60 (n)" = sum(GEC_Tscore < 60), "Global Executive Composite T-score > 60 (n)" = sum(GEC_Tscore >= 60))
## `summarise()` ungrouping output (override with `.groups` argument)
BRIEF_AgeApp_GEC_over60 %>%
kable() %>%
kable_styling(bootstrap_options = "striped")
| LanguageGroup | Global Executive Composite T-score < 60 (n) | Global Executive Composite T-score > 60 (n) |
|---|---|---|
| Typically Hearing | 39 | 5 |
| Early ASL | 15 | 0 |
| Later English | 21 | 8 |
| Later ASL | 10 | 4 |
GEC_60rr <- matrix(c(39,5,18,3, 21, 8, 11, 5), 4, 2, byrow=TRUE)
dimnames(GEC_60rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "GEC T-score" = c("Not Elevated", "Elevated"))
riskratio.small(GEC_60rr)
## $data
## GEC T-score
## Group Not Elevated Elevated Total
## Typically Hearing 39 5 44
## Early ASL 18 3 21
## Later English 21 8 29
## Later ASL 11 5 16
## Total 89 21 110
##
## $measure
## risk ratio with 95% C.I.
## Group estimate lower upper
## Typically Hearing 1.000000 NA NA
## Early ASL 1.071429 0.2823442 4.065814
## Later English 2.068966 0.7503603 5.704751
## Later ASL 2.343750 0.7804552 7.038410
##
## $p.value
## two-sided
## Group midp.exact fisher.exact chi.square
## Typically Hearing NA NA NA
## Early ASL 0.73734103 0.7063030 0.73735891
## Later English 0.09251239 0.1170560 0.07626226
## Later ASL 0.09611194 0.1124258 0.06757726
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"
#DHH groups not significantly more likely to have elevated GEC T-scores relative to TH group, but the two later groups are close
BRIEF_AgeApp_Inhibit_over60 <- BRIEF_AgeApp %>%
group_by(LanguageGroup) %>%
summarise("Inhibit T-score < 60 (n)" = sum(Inhibit_Tscore < 60), "Inhibit T-score > 60 (n)" = sum(Inhibit_Tscore >= 60))
## `summarise()` ungrouping output (override with `.groups` argument)
BRIEF_AgeApp_Inhibit_over60 %>%
kable() %>%
kable_styling(bootstrap_options = "striped")
| LanguageGroup | Inhibit T-score < 60 (n) | Inhibit T-score > 60 (n) |
|---|---|---|
| Typically Hearing | 39 | 5 |
| Early ASL | 13 | 2 |
| Later English | 25 | 4 |
| Later ASL | 9 | 5 |
Inhibit_60rr <- matrix(c(39,5,17,4, 25, 4, 11, 5), 4, 2, byrow=TRUE)
dimnames(Inhibit_60rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "Inhibit T-score" = c("Not Elevated", "Elevated"))
riskratio.small(Inhibit_60rr)
## $data
## Inhibit T-score
## Group Not Elevated Elevated Total
## Typically Hearing 39 5 44
## Early ASL 17 4 21
## Later English 25 4 29
## Later ASL 11 5 16
## Total 92 18 110
##
## $measure
## risk ratio with 95% C.I.
## Group estimate lower upper
## Typically Hearing 1.000000 NA NA
## Early ASL 1.428571 0.4269974 4.779458
## Later English 1.034483 0.3028657 3.533430
## Later ASL 2.343750 0.7804552 7.038410
##
## $p.value
## two-sided
## Group midp.exact fisher.exact chi.square
## Typically Hearing NA NA NA
## Early ASL 0.42858263 0.4545627 0.40157575
## Later English 0.76192336 1.0000000 0.75736046
## Later ASL 0.09611194 0.1124258 0.06757726
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"
#DHH groups *not* significantly more likely to have elevated Inhibit T-scores relative to TH group, but the Later ASL group close
BRIEF_AgeApp_Shift_over60 <- BRIEF_AgeApp %>%
group_by(LanguageGroup) %>%
summarise("Shift T-score < 60 (n)" = sum(Shift_Tscore < 60), "Shift T-score > 60 (n)" = sum(Shift_Tscore >= 60))
## `summarise()` ungrouping output (override with `.groups` argument)
BRIEF_AgeApp_Shift_over60 %>%
kable() %>%
kable_styling(bootstrap_options = "striped")
| LanguageGroup | Shift T-score < 60 (n) | Shift T-score > 60 (n) |
|---|---|---|
| Typically Hearing | 42 | 2 |
| Early ASL | 15 | 0 |
| Later English | 26 | 3 |
| Later ASL | 11 | 3 |
Shift_60rr <- matrix(c(42,2,19,2, 26, 2, 12, 4), 4, 2, byrow=TRUE)
dimnames(Shift_60rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "Shift T-score" = c("Not Elevated", "Elevated"))
riskratio.small(Shift_60rr)
## $data
## Shift T-score
## Group Not Elevated Elevated Total
## Typically Hearing 42 2 44
## Early ASL 19 2 21
## Later English 26 2 28
## Later ASL 12 4 16
## Total 99 10 109
##
## $measure
## risk ratio with 95% C.I.
## Group estimate lower upper
## Typically Hearing 1.000000 NA NA
## Early ASL 1.428571 0.2158668 9.454055
## Later English 1.071429 0.1599591 7.176581
## Later ASL 3.750000 0.7586056 18.537301
##
## $p.value
## two-sided
## Group midp.exact fisher.exact chi.square
## Typically Hearing NA NA NA
## Early ASL 0.48397436 0.58920596 0.43476804
## Later English 0.66760563 0.63954549 0.63902828
## Later ASL 0.04238826 0.03838937 0.01951748
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"
#Later ASL group significantly more likely to have elevated Shift T-scores relative to TH group (3.75 times more likely) according to p-values (even though risk ratio CI does include 1)
BRIEF_AgeApp_EmoCtrl_over60 <- BRIEF_AgeApp %>%
group_by(LanguageGroup) %>%
summarise("Emotional Control T-score < 60 (n)" = sum(Emotional.Control_Tscore < 60), "Emotional Control T-score > 60 (n)" = sum(Emotional.Control_Tscore >= 60))
## `summarise()` ungrouping output (override with `.groups` argument)
BRIEF_AgeApp_EmoCtrl_over60 %>%
kable() %>%
kable_styling(bootstrap_options = "striped")
| LanguageGroup | Emotional Control T-score < 60 (n) | Emotional Control T-score > 60 (n) |
|---|---|---|
| Typically Hearing | 33 | 11 |
| Early ASL | 15 | 0 |
| Later English | 27 | 2 |
| Later ASL | 12 | 2 |
EmoCtrl_60rr <- matrix(c(33,11, 19,2, 27,2, 13,3), 4, 2, byrow=TRUE)
dimnames(EmoCtrl_60rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "Emotional Control T-score" = c("Not Elevated", "Elevated"))
riskratio.small(EmoCtrl_60rr)
## $data
## Emotional Control T-score
## Group Not Elevated Elevated Total
## Typically Hearing 33 11 44
## Early ASL 19 2 21
## Later English 27 2 29
## Later ASL 13 3 16
## Total 92 18 110
##
## $measure
## risk ratio with 95% C.I.
## Group estimate lower upper
## Typically Hearing 1.0000000 NA NA
## Early ASL 0.3571429 0.08683599 1.468873
## Later English 0.2586207 0.06177609 1.082695
## Later ASL 0.7031250 0.22460549 2.201125
##
## $p.value
## two-sided
## Group midp.exact fisher.exact chi.square
## Typically Hearing NA NA NA
## Early ASL 0.15834537 0.19439161 0.14463160
## Later English 0.05147564 0.06302486 0.04789045
## Later ASL 0.64844160 0.73965677 0.61273516
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"
#Later English borderline *less* likely than TH to have elevated Emotional Control T-scores (but there do seem to be an unexpectedly high number of TH kids in the "elevated" score range, and Later English group only on average 4 months older than TH kids)
BRIEF_AgeApp_WorkMem_over60 <- BRIEF_AgeApp %>%
group_by(LanguageGroup) %>%
summarise("Working Memory T-score < 60 (n)" = sum(Working.Memory_Tscore < 60), "Working Memory T-score > 60 (n)" = sum(Working.Memory_Tscore >= 60))
## `summarise()` ungrouping output (override with `.groups` argument)
BRIEF_AgeApp_WorkMem_over60 %>%
kable() %>%
kable_styling(bootstrap_options = "striped")
| LanguageGroup | Working Memory T-score < 60 (n) | Working Memory T-score > 60 (n) |
|---|---|---|
| Typically Hearing | 38 | 6 |
| Early ASL | 13 | 2 |
| Later English | 21 | 8 |
| Later ASL | 8 | 6 |
WorkMem_60rr <- matrix(c(38,6,17,4,21,8,9,7), 4, 2, byrow=TRUE)
dimnames(WorkMem_60rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "Working Memory T-score" = c("Not Elevated", "Elevated"))
riskratio.small(WorkMem_60rr)
## $data
## Working Memory T-score
## Group Not Elevated Elevated Total
## Typically Hearing 38 6 44
## Early ASL 17 4 21
## Later English 21 8 29
## Later ASL 9 7 16
## Total 85 25 110
##
## $measure
## risk ratio with 95% C.I.
## Group estimate lower upper
## Typically Hearing 1.000000 NA NA
## Early ASL 1.224490 0.3863952 3.880419
## Later English 1.773399 0.6865126 4.581043
## Later ASL 2.812500 1.1116392 7.115759
##
## $p.value
## two-sided
## Group midp.exact fisher.exact chi.square
## Typically Hearing NA NA NA
## Early ASL 0.58471003 0.71529371 0.57175432
## Later English 0.15825023 0.22343601 0.13850571
## Later ASL 0.02168355 0.02870431 0.01228576
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"
#Later ASL significantly more likely to have elevated Working Memory T-scores relative to TH group
BRIEF_AgeApp_PlanOrg_over60 <- BRIEF_AgeApp %>%
group_by(LanguageGroup) %>%
summarise("Plan/Organize T-score < 60 (n)" = sum(Plan.Organize_Tscore < 60), "Plan/Organize T-score > 60 (n)" = sum(Plan.Organize_Tscore >= 60))
## `summarise()` ungrouping output (override with `.groups` argument)
BRIEF_AgeApp_PlanOrg_over60 %>%
kable() %>%
kable_styling(bootstrap_options = "striped")
| LanguageGroup | Plan/Organize T-score < 60 (n) | Plan/Organize T-score > 60 (n) |
|---|---|---|
| Typically Hearing | 37 | 7 |
| Early ASL | 14 | 1 |
| Later English | 19 | 10 |
| Later ASL | 10 | 4 |
PlanOrg_60rr <- matrix(c(37,7,18,3, 19, 10, 11, 5), 4, 2, byrow=TRUE)
dimnames(PlanOrg_60rr) <- list("Group" = c("Typically Hearing", "Early ASL", "Later English", "Later ASL"), "Plan/Organize T-score" = c("Not Elevated", "Elevated"))
riskratio.small(PlanOrg_60rr)
## $data
## Plan/Organize T-score
## Group Not Elevated Elevated Total
## Typically Hearing 37 7 44
## Early ASL 18 3 21
## Later English 19 10 29
## Later ASL 11 5 16
## Total 85 25 110
##
## $measure
## risk ratio with 95% C.I.
## Group estimate lower upper
## Typically Hearing 1.0000000 NA NA
## Early ASL 0.8035714 0.2305467 2.800851
## Later English 1.9396552 0.8336220 4.513152
## Later ASL 1.7578125 0.6500190 4.753561
##
## $p.value
## two-sided
## Group midp.exact fisher.exact chi.square
## Typically Hearing NA NA NA
## Early ASL 0.89457715 1.00000000 0.86529254
## Later English 0.07860199 0.09078038 0.06617443
## Later ASL 0.22088878 0.27301329 0.18894147
##
## $correction
## [1] FALSE
##
## attr(,"method")
## [1] "small sample-adjusted UMLE & normal approx (Wald) CI"
#DHH groups *not* significantly more likely to have elevated Inhibit T-scores relative to TH group